GIFT: A GEOSPATIAL IMAGE AND VIDEO FILTERING TOOL FOR COMPUTER VISIONAPPLICATIONS WITH GEO-TAGGED MOBILE VIDEOS

Yinghao Cai, Ying Lu, Seon Ho Kim, Luciano Nocera, Cyrus Shahabi

Integrated Media Systems CenterUniversity of Southern California, Los Angeles, CA 90089{yinghaoc, ylu720, seonkim, nocera, shahabi}@usc.edu

ABSTRACT

In this paper, we propose a novel geospatial image andvideo filtering tool (GIFT) to select the most relevant inputimages and videos for computer vision applications with geo-tagged mobile videos. GIFT tightly couples mobile medi-a content and their geospatial metadata for fine granularityvideo manipulation and intelligently indexes FOVs (Field ofView) to deal with large volumes of data. To demonstrate theeffectiveness of GIFT, we introduce an end-to-end applicationthat utilizes mobile videos to achieve persistent target track-ing over large space and time. Our experimental results showpromising performance of vision applications with GIFT interms of lower communication load, improved efficiency, ac-curacy and scalability.

Index Terms— geo-tagged, geospatial metadata, videodatabase, persistent tracking, image filtering

1. INTRODUCTION

In the past decade, we have witnessed rapid advances in mul-timedia data collection and management technologies as wellas computer vision applications for the analysis of the collect-ed data. However, there exists an obvious gap between thetwo well-known fields. Computer vision algorithms main-ly focus on the analysis of a given set of input images andvideos without considering what would be the most effectiveinput dataset for the analysis; while data management tech-niques concentrate on image and video management withoutproviding the search and query capabilities that would be use-ful for vision applications. The result is the inability to utilizethe full potentials of the underlying techniques in both fields.

A number of trends have recently emerged. First, weare experiencing enormous growth in the amount of im-ages/videos collected from various sources such as CCTVcameras and open sources like ubiquitous mobile devices.

This research has been funded in part by NSF grants IIS-1320149 andCNS-1461963, the USC Integrated Media Systems Center (IMSC), and un-restricted cash gifts from Google, Northrop Grumman and Oracle. Any opin-ions, findings, and conclusions or recommendations expressed in this mate-rial are those of the author(s) and do not necessarily reflect the views of anyof the sponsors such as the National Science Foundation.

Second, the continuous fusion of geospatial metadata (e.g.,camera location, viewing direction, textual keywords, etc)with video at a fine granular level has become feasible andtransparent for users, leading to the concept of geo-taggedvideos or sensor-rich videos [1, 2]. Many studies focusedon extracting geospatial metadata from images/videos [1, 3].Third, it is observed that the geographical properties of im-ages/videos provide context information for humans to bet-ter understand the images/videos, such as a panoramic viewmapped on a specific location (e.g., Google Street View).

However, even though these complementary metadata areincreasingly available, they have been largely underutilized,which in some cases has led to sub-optimal and ad-hoc da-ta management solutions to be incorporated in computer vi-sion applications. In this paper, we propose a novel Geospa-tial Image and Video Filtering Tool (GIFT) which providesa general and systematic means to select the most relevan-t input images/videos for vision algorithms. GIFT harnessesand manages geospatial metadata acquired during recordingtime and intelligently indexes FOVs (Field of View) to dealwith large volumes of data. Although geospatial metadatahas been used in Crandall et al. [4] for predicting location-s from photos and Lu et al. [5] for trip planning, our workfocuses on providing vision applications with the most rele-vant input dataset to facilitate image/video analytics, which isdifferent from the focus of [4, 5]. The innovative claims ofGIFT include: 1) maximizing the utility of existing geospa-tial metadata acquisition and extraction technologies, 2) effi-ciently managing media content with the associated geospa-tial metadata collected from mobile devices for indexing andsearching, 3) supporting various vision applications to enablescalable image/video analytics.

To demonstrate the effectiveness of GIFT, we apply GIFTto a computer vision application aimed at persistent trackingusing mobile videos. Figure 1 illustrates how GIFT is ap-plied to the persistent tracking problem. When the target islost by the tracker, GIFT is used to select the most relevan-t video segments close in space and time to allow automaticre-identification and subsequent tracking of the target. Wedemonstrate that with GIFT, the overall performance of per-

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Fig. 1. Illustration of GIFT for persistent tracking. If the target exits the FOV of one video, GIFT effectively selects a smallnumber of video segments which may cover this target.

sistent tracking is improved in terms of:

∙ Efficiency and lower communication cost: GIFT effective-ly selects a small number of the most relevant video seg-ments as the input to the tracking task. Therefore, therunning time efficiency of the tracking system is improvedwhile the amount of video data needed to be transferredover the network is drastically reduced.

∙ Re-identification and tracking accuracy: It is known thatre-identifying targets in a large video repository is error-prone. GIFT makes the tracking system more accurate byreducing the number of unnecessary target matching.

The general problem of persistent tracking is to continu-ously track a target, e.g., car or pedestrian, over large spaceand time. Most of the previous work addressed the problemof persistent tracking using multiple stationary cameras [6]. Itis generally assumed that these video feeds are synchronizedand their camera network topology is known. The camer-a network topology is used to predict in which camera thetarget will reappear. Existing work [7] relies on statistical ob-servations over a period of time to infer the camera topologywhich cannot handle mobile videos captured in a casual waywith various shooting directions and moving trajectories.

2. GEOSPATIAL IMAGE AND VIDEO FILTERINGTOOL

2.1. Overview

The GIFT framework presented in Figure 2 has three compo-nents: vision applications, GIFT, and the video database. Thevideo database (e.g., MySQL) stores the geospatial metada-ta of videos. Video contents are stored as files. When thevision application requires a set of images/videos to process(e.g., generating a panoramic image), it sends a query requestto GIFT. GIFT performs the query and returns a set of im-ages/videos as the query result. The image/video results aretransferred to the application over the network.

2.2. Video Database

In this paper, each video frame is geo-tagged and representedas an FOV model as shown in Figure 3. Camera location 𝑃 isthe latitude and longitude coordinates read from the GPS sen-sor of the smartphone. Camera viewing direction 𝑑 is the ori-entation angle provided by the digital compass. Camera view-able angle 𝛼 describes the angular extent of the scene viewed

by the camera. Visible distance 𝑅 is defined as the maxi-mum distance which can be viewed by the camera. Basedon camera direction 𝑑, we can determine 𝜃 which is the an-gle of the view direction with respect to the North direction.Therefore, the FOV model of each frame is represented as𝑓(𝑃, 𝜃, 𝛼,𝑅). The video repository 𝒱 is then represented asan FOV database ℱ = {𝑓𝑣𝑖

∣∀𝑣𝑖 ∈ 𝒱}, where 𝑣𝑖 is the videoframe. The timestamp 𝑡 and camera tilt of each video frameare captured as well in the metadata. It should be noted thatthe camera FOV is generally a 3D object. In this paper, weuse a 2D representation of FOV for simplicity.

2.3. Modules of GIFT

GIFT includes three main modules: Logical Operations, Fil-tering Functions, and Sorting and Extraction.

2.3.1. Logical Operations Module

The Logical Operations Module 1) receives query requestsfrom applications, where a query request is a logical combi-nation of a set of filtering functions, 2) converts the specificquery request to a set of query functions (i.e., filtering func-tions) to be executed in the Filtering Functions Module, 3)gets the FOV result sets of the filtering functions and com-bines the FOV result sets into a result set and 4) sends theresult set to the Sorting Module.

GIFT supports three basic logical operations, AND, OR,XOR, for combining the filtering functions. The query re-quest is a logical expression of the specified filtering func-tions, e.g., (𝐹1 𝐴𝑁𝐷 𝐹2), ((𝐹1 𝑂𝑅 𝐹2) 𝐴𝑁𝐷 𝐹3), where𝐹1, 𝐹2 and 𝐹3 are filtering functions defined in Section 2.3.2.

2.3.2. Filtering Functions Module

The Filtering Functions Module provides query functionswith different filtering options based on FOVs stored in thedatabase. We provide three types of query functions depend-ing on the type of filtering options: spatial queries, directionalqueries, temporal queries and two types of queries in terms ofquery frequency (snapshot or continuous). These five typesof queries can be combined for different purposes.Spatial queries The spatial queries filter FOVs with spatialinformation. Spatial queries include point queries, circle orrectangle range queries, kNN queries, etc.

1. Point queries are defined as finding FOVs that contain theuser-specified query point 𝑞.

FOV Database

VisionApplications GIFT

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Fig. 2. GIFT Framework

camera location (longitude, latitude)

visible distance

camera direction

viewable angle

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angle of the view direction w.r.t North

Fig. 3. Illustration of the FOV model.

2. Circle or rectangle range queries find FOVs that overlapwith the user-specified query circle or rectangle 𝑞𝑟.

3. kNN queries find 𝑘 closest FOVs that show the user-specified query point 𝑞, where the closeness from FOV 𝑓to the query point 𝑞 is measured by the Euclidean distance.

Directional queries find FOVs which the orientations over-lap with the user-specified directions. The direction requestscan be directional internal (e.g., the Northeast), inwards (i.e.,FOVs that pointing toward the query point) and outwards (i.e.,FOVs that pointing outward the query point).Temporal queries find FOVs that are recorded within theuser-specified query time. The query time can be specifiedas a specific timestamp or time interval.Snapshot queries are evaluated/processed once assumingthat the query results do not change.Continuous queries require continuous evalua-tion/processing as the query results become invalid withthe change of information (e.g., the change of querylocations, etc).

2.3.3. Indexing Module

To accelerate query processing for filtering functions, we sup-port FOV indexing in GIFT. Since FOVs are spatial objectsin the shape of pie slice, we use R-tree [8] to enclose thearea of each FOV with its Minimum Bounding Rectangle (M-BR). With R-trees, we use the pruning techniques supportedby MySQL for our spatial filtering functions.

2.3.4. Sorting and Extraction Module

GIFT provides three basic sorting functions: 1) sorting by dis-tance, where the distance is the Euclidean distance betweenthe FOV’s camera location and the query point; 2) sorting by

orientation, which is the angle between the FOV’s orientationand the query direction; and 3) sorting by time, i.e., the close-ness of the FOV’s timestamp to the query time.

The query results of GIFT consist of a set of FOVs. Wefurther combine continuous FOVs that are in the same origi-nal video into video segments using FFMPEG library.

2.4. Example of GIFT application

To illustrate how GIFT improves the efficiency of vision ap-plications, we show an application of GIFT in point panoramageneration. Point panorama generation consists in generatinga panoramic image as seen from a user-specified query point.The main idea here is to select video frames which the cam-era positions are located within a predefined threshold radius𝑟 (e.g., 10 meters, which is a typical GPS error margin) fromthe query point 𝑞. We then divide 360 degrees into 𝑛 groupsaround the query point 𝑞 based on the directions of FOVs. Foreach group, the best matching FOV is selected. The “best”metric is measured by a linear combination of the distancefrom the FOV’s camera location to the point 𝑞 and the direc-tion difference between the FOV’s orientation and the groupdirection.

Using GIFT, we apply circle range query first. Sortingfunction based on the linear combination of the distance andorientation difference for each group is then applied. Asshown in Figure 4, without GIFT, the stitching algorithm hasto use all 228 video frames. GIFT only selects 13 frameswithout loss of image quality. It demonstrates that GIFT e-liminates redundant video frames for efficient panorama gen-eration.

3. USE CASE: PERSISTENT TRACKING

In this section, we focus on a case study, persistent track-ing, that can showcase the effectiveness of GIFT in visionapplications. Figure 5 shows an overview of the persisten-t tracking system. We first tag a target which is then auto-matically tracked in the videos. If the tracker ends, e.g., thetracker reaches the last frame of the video or the tracker losesthe target, the persistent tracking system issues a request to

(a) Without GIFT, 228 FOVs were used

(b) With GIFT, only 13 FOVs were selected

Fig. 4. GIFT in point panorama generation.

GIFT to receive videos that may cover the target to allow forsubsequent reacquisition and tracking. Video selection, re-identification and tracking are repeated and the target is per-sistently tracked - in an automatic fashion - across multiplevideo segments from different videos.

3.1. GIFT in Persistent Tracking

If the tracker ends at timestamp 𝑡𝑜, based on the last observedlocation 𝑃𝑜 and the moving direction of the target, we predictthe next possible locations of the target in timestamp 𝑡′𝑜 usinga constant velocity model. Suppose the predicted location ofthe target is 𝑃 ′

𝑜 , we combine spatial queries with temporalqueries in GIFT to actively select video frames which maycover this target:

PointQuery(𝑃𝑜, 𝑡𝑜)←−{𝑓𝑣𝑖 ∈ ℱ∣ 𝑓𝑣𝑖 ∩ 𝑃 ′

𝑜 ∕= ∅, 0 ≤ 𝑡′𝑜 − 𝑡𝑜 < 𝜏}RangeQuery(𝑃𝑜, 𝑡𝑜)←−

{𝑓𝑣𝑖 ∈ ℱ∣ 𝑓𝑣𝑖 ∩ 𝑄𝑟(𝑃′𝑜, 𝑟) ∕= ∅, 0 ≤ 𝑡′𝑜 − 𝑡𝑜 < 𝜏}

(1)where 𝑄𝑟(𝑃

′𝑜, 𝑟) is a query circle with the center point 𝑃 ′

𝑜 andthe radius 𝑟. ∩ is the geographical overlap. Point query find-s all FOVs which overlap with the predicted location of thetarget. Range query finds all FOVs which overlap with thecircle 𝑄𝑟(𝑃

′𝑜, 𝑟). Since long-term prediction of the target lo-

cation may not be reliable, we limit the temporal range withina bound 𝜏 . An illustration of the point query and range queryin persistent tracking is shown in Figure 6. Consecutive FOVsare grouped into video segments for tracking in the ExtractionModule of GIFT.

The target reacquisition module (Section 3.2) has to ex-amine the reappearance of this target in all video segmentsreturned by GIFT. We further leverage the observation thatit is advantageous to have a consistent viewpoint to observethe target for persistent tracking. Here, “viewpoint” refers tothe camera’s azimuth with respect to the target. We combinedirection query in GIFT to find FOVs which the camera ori-entations overlap with the target’s moving direction within arange:

DirectionQuery(𝜃𝑜)←−{𝑓𝑣𝑖 ∈ ℱ∣ 𝜃𝑜 − 𝛿 ≤ 180 + 𝑓𝑣𝑖(𝜃) ≤ 𝜃𝑜 + 𝛿}

(2)where 𝑓𝑣𝑖

(𝜃) is the direction of the FOV with respect to theNorth direction. 𝜃𝑜 is the moving direction of the target. 𝛿is a direction margin since a precise moving direction of thetarget may not be available. By applying direction query on

Fig. 5. An overview of the persistent tracking system.

the top of the results of point query and range query, we get aconsistent frontal view of the target. Finally, query results aresorted in the Sorting Module of GIFT according to the dis-tance between the camera location and the predicted locationof the target.

Different combinations of queries are defined in GIFT. In-dexing FOVs is performed to speed up the queries. Since thetimestamp of each video frame is stored in the metadata, itnaturally resolves the problem of video synchronization.

3.2. Target Tracking and Reacquisition

If the tracker [9] reaches the last frame of the video segmentor the confidence of the tracker has been low for 10 frames,e.g., the target becomes occluded or exits the view, GIFT isapplied to select video segments which may cover this target.We track each target in the first couple of frames of videosegments returned from GIFT in order to reacquire the target.Without frame-by-frame spatial continuity as in single cameratracking, target reacquisition across views is a difficult prob-lem. The target is reacquired by choosing the track with thelargest affinity score of appearance and position. In comput-ing the appearance affinity, GIFT largely reduces many un-necessary target matching by filtering out geographically farapart targets. Furthermore, the latitude and longitude of thetracks can be inferred from camera calibration [10]. It is pos-sible to compute the position affinities of the tracks based ongeo-coordinates. Therefore, the accuracy and efficiency oftarget reacquisition are improved with GIFT.

Once the target is reacquired in one video segment, thetracker is then switched to this video segment where trackingis continued. If the target is not reacquired, e.g, no observa-tions from the query results can be matched with this target,the persistent tracking task is terminated.

4. EXPERIMENTAL RESULTS AND ANALYSIS

We carry out experiments on both synthetic datasets and real-world datasets to evaluate the effectiveness of the proposedGIFT. Here, we aim to answer the following questions:∙ How effective is GIFT at filtering out unrelated video seg-

ments? (performance)

video

last observed location

predicted location

video

last observed location

predicted location

(a) Point Query (b) Range Query

Fig. 6. An illustration of point and range query.

∙ How complete and accurate are the video segments resultsreturned by GIFT that may cover the target? (accuracy)

We introduce two baseline methods for comparison. Forboth baseline methods, the camera viewable scene is de-scribed as a circular region centered around the camera lo-cation. In this case, the camera view direction is not known.In the remainder of this paper, we call this circular viewableregion CircleScene. More specifically:

∙ Baseline 1: Each video clip is represented by a single cam-era location with a CircleScene. The location of the firstvideo frame defines the location for the entire video.

∙ Baseline 2: Each video clip is represented by a sequenceof camera locations with associated CircleScenes.

The rationale for using a single CircleScene representa-tion for the entire video in Baseline 1 is motivated by the factthat many online media management systems, e.g., YouTube,use a single camera location to represent an entire video. Onthe contrary, Baseline 2 provides a fine-grained spatial res-olution by associating CircleScene to each frame. Geospa-tial search results for both baseline methods rely on a rangequery that returns video segments for videos frames whichCircleScene intersects the predicted location of the target.

We return video frames within a temporal range of[𝑡𝑜, 𝑡𝑜+𝜏 ] which may cover the target with 𝜏 = 100 frames inall experiments. The radius of the CircleScene model and therange query (denoted by 𝑟 in Eq. 1) in GIFT and the baselinemethods are set to 0.1 Km. We use the same parameter set-tings in the baseline methods as GIFT for a fair comparison.

Consecutive FOVs are grouped into video segments inGIFT for target reacquisition and tracking. We eliminatevideo segments which are shorter than 5 seconds. In the syn-thetic dataset, one random video segment is chosen from thereturned video segments to resume target tracking. In the real-world dataset, target reacquisition is performed in all the re-turned video segments to reacquire the target.

4.1. Evaluation Metrics

Communication load, Precision and Recall are used to evalu-ate the proposed GIFT. Without loss of generality, communi-cation load is defined as the number of FOVs transferred overthe network. Precision and Recall are defined as follows:

Precision =∣FOVR ∩GT∣

∣FOVR∣Recall =

∣FOVR ∩GT∣∣GT∣

Table 1. Summary of the Synthetic Datasettotal # of FOVs 0.48Milliontotal # of videos 500

FOV # per second 1average length of the video (hours) 0.28

average camera moving speed(km/h) 5average camera rotation speed (degree/s) 11

viewable distance 𝑅 (meters) 100viewable angle 𝛼 (degrees) 60

where ∣FOVR∣ is the number of FOVs returned from GIFT orthe baseline methods. ∣GT∣ is the number of FOVs from theground truth which covers a target. It should be noted that inour experiments, all baseline methods and GIFT return FOVsbased on the predicted location of the target. The target isnot always visible to the cameras since cameras are sparselylocated. On the other hand, ∣GT∣ is the number of frameswhere the FOVs truly cover the ground truth location of thetarget. To compute ∣GT∣, we replace the predicted location ofthe target with the ground truth location.

Precision is the ratio of retrieved relevant FOVs to all re-trieved FOVs. A lower value of precision implies the searchresult set contains a large number of invisible regions in track-ing. Precision evaluates the accuracy of the query results. Re-call is the ratio of retrieved relevant FOVs to all relevant FOVsin the dataset. A lower recall value means more regions thatshould be returned as visible are ignored. Recall evaluates thecompleteness of the query results.

4.2. Results with Synthetic Dataset

We generated synthetic mobile video metadata and trajecto-ries of twenty targets using the generation algorithm methodin [11]. The synthetic video metadata generated are of real-istic geographical properties which is summarized in Table 1.The FOVs are generated uniformly distributed around an areaof 1 Km by 1 Km initially. We assume that the mobile videosare taken by 500 freely moving people walking randomly inthe area at an average speed of 5.13 Km/h [11]. Therefore,there are altogether 500 camera trajectories.

We differentiated GIFTdir with GIFTnodir depending onif direction is used in the experiments (Eq. 2). The direc-tion margin 𝛿 in Eq. 2 was set to 15 degrees. Figure 8 showsthe communication load per target. GIFT performed far bet-ter than baseline methods in all cases. It should be noted thatit is not always possible to apply GIFTdir for all analysis be-cause the frontal view of target may not be always available invideos. Nonetheless, GIFTdir is promising if the right datasetis available.

Figure 9 shows the average Precision and Recall of theGIFTnodir and the baseline methods after tracking 20 target-s. It was observed that the precision of GIFT was greatlyhigher compared with baseline methods and GIFT effective-ly filtered out unrelated FOVs while returning a reasonablycomplete set of the FOVs which may cover the target. Recallgreatly depends on how well the tracking algorithm predictsthe location of a target. We used a simple method in the sim-ulated dataset. Any advanced prediction method can improve

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Fig. 7. Snapshots of the persistent tracking system.

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Fig. 8. Comparison of communication loads of 20 targets.

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Fig. 9. Average Precision and Recall of 20 targets.

the Recall rate, which is beyond the scope of this paper.

4.3. Results with Real-World Dataset

In the experiments with real-world dataset, we used a mo-bile media management system, named MediaQ [2] to collectand store mobile videos. Videos were captured using smart-phones by five people. We performed persistent tracking for asingle target for 5 minutes. Figure 10 shows the results of theaccumulated communication loads using the real-world sce-nario. Note that in this experiment, we used 30 FOVs per sec-ond for tracking. We observed that GIFT effectively reducedthe number of frames transferred over the network which inturn reduced number of unnecessary target matching in targetreacquisition. By reducing the number of target matching, theaccuracy of tracking and reacquisition were improved. Ourpersistent tracking system successfully tracked the target us-ing GIFT as shown in Figure 7. The main focus of this paperis GIFT and its applications to vision applications. A thor-ough description and evaluation of the used tracking algorith-m is beyond the scope of this paper.

5. CONCLUSIONS

In this paper, we presented a novel geospatial image and videofiltering tool (GIFT) to select the most relevant set of input

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Fig. 10. A comparison of accumulated communication loadsusing real-world dataset.

images and videos for computer vision applications. For theevaluation of GIFT, persistent tracking application was intro-duced and evaluated using both synthetic and real dataset. Weobserved an improved performance of vision applications interms of lower communication load, an improved efficiencyand accuracy to handle a large amount of video data.

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