Abstract
This paper proposes a multiple-object detection and tracking method that explicitly handles dynamic occlusions. A context-based multiple-cue detector is proposed to detect occluded vehicles (occludees). First, we detect and track fully-visible vehicles (occluders). Occludee detection adopts those occluders as priors. Two classifiers for partially-visible vehicles are trained to use appearance cues. Disparity is adopted to further constrain the occludee locations. A detected occludee is then tracked by a Kalman-based tracking-by-detection method. As dynamic occlusions lead to role changes for occluder or occludee, an integrative module is introduced for possibly switching occludee and occluder trackers. The proposed system was tested on overtaking scenarios. It improved an occluder-only tracking system by over 10% regarding the frame-based detection rate, and by over 20% regarding the trajectory detection rate. The occludees are detected and tracked in the proposed method up to 7 seconds before they are picked up by occluder-only method.
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Tao, J., Enzweiler, M., Franke, U., Pfeiffer, D., Klette, R. (2015). What Is in Front? Multiple-Object Detection and Tracking with Dynamic Occlusion Handling. In: Azzopardi, G., Petkov, N. (eds) Computer Analysis of Images and Patterns. CAIP 2015. Lecture Notes in Computer Science(), vol 9256. Springer, Cham. https://doi.org/10.1007/978-3-319-23192-1_2
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DOI: https://doi.org/10.1007/978-3-319-23192-1_2
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