Multi-Object tracking using Multi-Channel Part Appearance Representation

Abstract : Appearance based multi-object tracking (MOT) is a challenging task, specially in complex scenes where objects have similar appearance or are occluded by background or other objects. Such factors motivate researchers to propose effective trackers which should satisfy real-time processing and object trajectory recovery criteria. In order to handle both mentioned requirements, we propose a robust online multi-object tracking method that extends the features and methods proposed for re-identification to MOT. The proposed tracker combines a local and a global tracker in a comprehensive two-step framework. In the local tracking step, we use the frame-to-frame association to generate online object trajectories. Each object trajectory is called tracklet and is represented by a set of multi-modal feature distributions modeled by GMMs. In the global tracking step, occlusions and mis-detections are recovered by tracklet bipartite association method based on learning Mahalanobis metric between GMM components using KISSME metric learning algorithm. Experiments on two public datasets show that our tracker performs well when compared to state-of-the-art tracking algorithms.
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https://hal.inria.fr/hal-01651938
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Submitted on : Wednesday, November 29, 2017 - 4:56:35 PM
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Thi Lan Anh Nguyen, Furqan Khan, Farhood Negin, François Bremond. Multi-Object tracking using Multi-Channel Part Appearance Representation. AVSS 2017 : 14-th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Aug 2017, Lecce Italy. ⟨hal-01651938⟩

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