Multi-Object tracking using multi-channel part appearance representation
| Autor: | Furqan M. Khan, Francois Bremond, Farhood Negin, Nguyen Thi Lan Anh |
|---|---|
| Přispěvatelé: | Spatio-Temporal Activity Recognition Systems (STARS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) |
| Rok vydání: | 2017 |
| Předmět: |
Computer science
business.industry BitTorrent tracker [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] 020207 software engineering Tracking system 02 engineering and technology Object (computer science) Tracking (particle physics) Feature (computer vision) Video tracking Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Trajectory 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | AVSS AVSS 2017 : 14-th IEEE International Conference on Advanced Video and Signal-Based Surveillance AVSS 2017 : 14-th IEEE International Conference on Advanced Video and Signal-Based Surveillance, Aug 2017, Lecce Italy |
| DOI: | 10.1109/avss.2017.8078552 |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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