Multi-class Multi-object Tracking Using Changing Point Detection
| Autor: | Songguo Jin, Phill Kyu Rhee, Mi Young Nam, Enkhbayar Erdenee, Young Giu Jung, Byungjae Lee |
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| Rok vydání: | 2016 |
| Předmět: |
Motion detector
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 020206 networking & telecommunications 02 engineering and technology Object (computer science) Tracking (particle physics) Class (biology) Convolutional neural network Video tracking 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Computer vision Point (geometry) Artificial intelligence business |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319488806 ECCV Workshops (2) |
| DOI: | 10.1007/978-3-319-48881-3_6 |
| Popis: | This paper presents a robust multi-class multi-object tracking (MCMOT) formulated by a Bayesian filtering framework. Multi-object tracking for unlimited object classes is conducted by combining detection responses and changing point detection (CPD) algorithm. The CPD model is used to observe abrupt or abnormal changes due to a drift and an occlusion based spatiotemporal characteristics of track states. The ensemble of convolutional neural network (CNN) based object detector and Lucas-Kanede Tracker (KLT) based motion detector is employed to compute the likelihoods of foreground regions as the detection responses of different object classes. Extensive experiments are performed using lately introduced challenging benchmark videos; ImageNet VID and MOT benchmark dataset. The comparison to state-of-the-art video tracking techniques shows very encouraging results. |
| Databáze: | OpenAIRE |
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