Supporting Multi Camera Tracking by Monocular Deformable Graph Tracking

Abstract The reliable detection and tracking of objects, in particu-lar humans, in video sequences is a requirement for videosurveillance systems. This step enables automated threatdetection systems to analyze trajectories and motion pat-terns. Thereby systems based on multiple overlapping ﬁeldsof view have emerged in the last years. These are usually re-lying on simple foreground masks and discard texture infor-mation. In this work we propose to incorporate a 2D trackerinto a multiple camera tracking system to avoid ID confu-sions during tracking. As both partial and total occlusionsoccur, trackers based on holistic person models usually fail.Therefore we propose to model object regions with an elas-tic feature graph, where the nodes are represented by SIFTfeatures and are updated during the tracking process. Thisrepresentation will enhance tracking performance in the 2Dand can be applied in any view of a multi camera trackingsystem. 1. Introduction Large public spaces have always been crucial to anysociety. It is in public spaces like town squares, airports,train stations, government buildings and shopping centersthat we travel, meet, conduct our business and interactas a society. However, these spaces are therefore alsovulnerable to abuse and attack. The safety concerns rangefrom minor nuisances like pick-pockets to graver threadslike terrorist attacks.In order to reduce security risks, many of these places areunder surveillance by video cameras (CCTV). These aretypically monitored by police or private security personnel.Unfortunately human ability to track many different scenes,individuals, and interactions on multiple screen and invarious scenes is limited. A human operator can usuallyobserve only a fraction of a region covered by a sensornetwork. This leads naturally to blind spots which can betaken advantage of. By assuming a neutral appearance,criminals and terrorists are able to blend in with crowds.Drifting from one camera scene to another, they escapenotice by moving continuously from one screen to another.All persons in the scene are thus able to travel freelythrough the scenery. Only a very experienced operatormight notice them.To overcome these limits and to support security incomplex buildings and settings, several tools to supportand automatize tracking and recognition of people inmulti-camera scenarios have been investigated. Multi-layerhomography, based on Khans homography constraint[1],is usually applied in multi sensor networks, to reliablytrack persons [2]. Current systems can automatically trackpersons and detect a set of special events like abandonedbags, theft and loitering based on simple heuristics [3, 4]. This multi camera approach allows for good individualtracking under most circumstances. However, underinconvenient conditions the system can lose track of anindividual or confuses two tracked persons, and conse-quently confuses their assigned tracking ID. One of thesesituations is shown in ﬁg. 1, where two previously trackedpersons brieﬂy obscure each other before parting again.Due to their close proximity and optical obstruction, the 3Dtracking system registers the two individuals as one singleentity and discards the second tracking ID-number. Whenthey part, one person is assigned a new number while theother keeps the original tracking ID-number. To remedythis problem, we decided to introduce a second level oftracking. In addition to the existing multi-camera trackingsystem, we designed a secondary monocular trackingmechanism in order to resolve ambiguous situations duringand after occlusions. In this ﬁrst stage of development, wefocus on using robust graph representations to track andidentify individual persons. Such a graph consists of nodes,here represented by SIFT features, and edges, which areused to create geometrical relationships between the nodes.