Vehicle Re-identification for Automatic Video Traffic Surveillance

Zapletal, Dominik; Herout, Adam

doi:10.1109/cvprw.2016.195

Cited by 124 publications

(72 citation statements)

References 10 publications

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“…A typical Re-ID algorithm is based on appearance modeling and matching [38,39]. Appearance modeling often uses low-level features such as color, texture, gradient or a combination thereof to build more discriminative appearance descriptors [37,38]. Many successful Re-ID algorithms have been proposed for special target Re-ID systems [37][38][39][40], such as pedestrians and vehicles.…”

Section: Related Workmentioning

confidence: 99%

“…Appearance modeling often uses low-level features such as color, texture, gradient or a combination thereof to build more discriminative appearance descriptors [37,38]. Many successful Re-ID algorithms have been proposed for special target Re-ID systems [37][38][39][40], such as pedestrians and vehicles. Liu et al [37] exploited a spatio-temporal body-action model by using Fisher vector learning to solve the large appearance variation problem presented by a pedestrian.…”

Section: Related Workmentioning

confidence: 99%

“…Liu et al [37] exploited a spatio-temporal body-action model by using Fisher vector learning to solve the large appearance variation problem presented by a pedestrian. Zapletal et al [38] proposed an approach based on a linear regression model using color histograms and histograms of oriented gradients for vehicle re-identification in a multiple cameras scenario. Liu et al [39] proposed a fusion model of low-level features and high-level semantic attributes for vehicle Re-ID.…”

Section: Related Workmentioning

confidence: 99%

“…Motivated by the works in object re-identification [38,39] to build long-term object trajectories based on appearance modeling and matching, the fourth association stage of the proposed framework solves the assignment problem between the lost tracklets T , linking these fragmented tracklets, re-identifying the lost objects and thereby building longer trajectories. As targets in airborne videos have similar appearances, false tracklet linking might occur if only based on the appearance modeling.…”

Section: Stage 4: Globally Linking Fragmented Trackletsmentioning

confidence: 99%

“…Re-ID has been intensively studied for stationary inter-camera target associations [37] for long-term object tracking. A typical Re-ID algorithm is based on appearance modeling and matching [38,39]. Appearance modeling often uses low-level features such as color, texture, gradient or a combination thereof to build more discriminative appearance descriptors [37,38].…”

Section: Related Workmentioning

confidence: 99%

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A Hierarchical Association Framework for Multi-Object Tracking in Airborne Videos

Chen

Pennisi

et al. 2018

Remote Sensing

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Multi-Object Tracking (MOT) in airborne videos is a challenging problem due to the uncertain airborne vehicle motion, vibrations of the mounted camera, unreliable detections, changes of size, appearance and motion of the moving objects and occlusions caused by the interaction between moving and static objects in the scene. To deal with these problems, this work proposes a four-stage hierarchical association framework for multiple object tracking in airborne video. The proposed framework combines Data Association-based Tracking (DAT) methods and target tracking using a compressive tracking approach, to robustly track objects in complex airborne surveillance scenes. In each association stage, different sets of tracklets and detections are associated to efficiently handle local tracklet generation, local trajectory construction, global drifting tracklet correction and global fragmented tracklet linking. Experiments with challenging airborne videos show significant tracking improvement compared to existing state-of-the-art methods.

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