Kernel-Based 3D Tracking

Tyagi, Ambrish; Keck, Mark; Davis, James W.; Potamianos, Gerasimos

doi:10.1109/cvpr.2007.383501

Cited by 20 publications

(12 citation statements)

References 16 publications

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“…These (2D) image-plane trackers often run into problems such as target scale selection, target rotation, occlusion, view-dependence, and correspondence across views [2]. There are few 3D tracking approaches [2,3] that fuse results from independent 2D trackers to obtain 3D trajectories.…”

Section: Tracking With Camera Networkmentioning

confidence: 99%

Collaborative 3D Target Tracking in Distributed Smart Camera Networks for Wide-Area Surveillance

Kushwaha

Koutsoukos

2013

JSAN

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With the evolution and fusion of wireless sensor network and embedded camera technologies, distributed smart camera networks have emerged as a new class of systems for wide-area surveillance applications. Wireless networks, however, introduce a number of constraints to the system that need to be considered, notably the communication bandwidth constraints. Existing approaches for target tracking using a camera network typically utilize target handover mechanisms between cameras, or combine results from 2D trackers in each camera into 3D target estimation. Such approaches suffer from scale selection, target rotation, and occlusion, drawbacks typically associated with 2D tracking. In this paper, we present an approach for tracking multiple targets directly in 3D space using a network of smart cameras. The approach employs multi-view histograms to characterize targets in 3D space using color and texture as the visual features. The visual features from each camera along with the target models are used in a probabilistic tracker to estimate the target state. We introduce four variations of our base tracker that incur different computational and communication costs on each node and result in different tracking accuracy. We demonstrate the effectiveness of our proposed trackers by comparing their performance to a 3D tracker that fuses the results of independent 2D trackers. We also present performance analysis of the base tracker along Quality-of-Service (QoS) and Quality-of-Information (QoI) metrics, and study QoS vs. QoI trade-offs between the proposed tracker variations. Finally, we demonstrate our tracker in a real-life scenario using a camera network deployed in a building.

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Section: Tracking With Camera Networkmentioning

confidence: 99%

Collaborative 3D Target Tracking in Distributed Smart Camera Networks for Wide-Area Surveillance

Kushwaha

Koutsoukos

2013

JSAN

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“…Tyagi et al [41] extended KBOT to 3D by comc 2016 Information Processing Society of Japan bining evidence from multiple calibrated cameras. This algorithm uses a feature level fusion framework to track the target directly in 3D space, and could be bootstrapped with an automatic re-initialization technique based on clustering 3D point clouds of the foreground targets.…”

Section: Kernel-based Mean-shiftmentioning

confidence: 99%

“…So far, none tracking methods can handle this problem. Though there has been methods for re-initialization based on multiple cameras [41], [112], the re-initialization criterion for single camera is still an open challenge.…”

Section: Conclusion and Feature Directionsmentioning

confidence: 99%

Review on Kernel based Target Tracking for Autonomous Driving

Wang

Yue

Dong

et al. 2016

Journal of Information Processing

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Significant progress has been made in the field of autonomous driving during the past decades. However, fully autonomous driving in urban traffic is still extremely difficult in the near future. Visual tracking of vehicles or pedestrians is an essential part of autonomous driving. Among these tracking methods, kernel-based object tracking is an effective means of tracking in video sequences. This paper reviews the kernel theory adopted in target tracking of autonomous driving and makes a qualitative and quantitative comparison among several well-known kernel based methods. The theoretical and experimental analysis allow us to conclude that the kernel based online subspace learning algorithm achieves a good trade-off between the stability and real-time processing for target tracking in the practical application environments of autonomous driving. This paper reports on the result of evaluating the performances of five algorithms by using seven video sequences.

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“…The method in [17] fuses the results obtained by existing single-view tracking methods that are applied individually to each of the views. However, the limitations of single view methods in handling occlusions still affect the fused results.…”

Section: Related Workmentioning

confidence: 99%

Multicamera tracking of multiple humans based on colored visual hulls

Padeleris

Zabulis

Argyros

2013

2013 IEEE 18th Conference on Emerging Technologies &Amp; Factory Automation (ETFA)

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Detecting, localizing and tracking humans within an industrial environment are three tasks which are of central importance towards achieving automation in workplaces and intelligent environments. This is because unobtrusive, real-time and reliable person tracking provides valuable input to solving problems such as workplace surveillance and event/activity recognition and, also, contributes to safety and optimized use of resources. This paper presents a passive approach to the problem of person tracking that is based on a network of conventional color cameras. The proposed approach exhibits robustness to challenging conditions that are encountered in industrial environments due to illumination artifacts, occlusions and the highly dynamic nature of the observed scenes. The multiple views of the environment that the system employs are used to obtain a volumetric representation of the humans within it, in real-time. Although human tracking can be achieved based solely on such a volumetric representation, in demanding scenes, this information is not enough to recover from tracking failures. Thus, in this work, we collect and update a representation of the color appearance of the persons in the environment. The combination of volumetric and color information reinforces tracking robustness, even when a person is not visible by any of the cameras for extended time intervals. The proposed approach has been extensively evaluated in comparison with an existing state of the art method and pertinent results are reported.

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Kernel-Based 3D Tracking

Cited by 20 publications

References 16 publications

Collaborative 3D Target Tracking in Distributed Smart Camera Networks for Wide-Area Surveillance

Collaborative 3D Target Tracking in Distributed Smart Camera Networks for Wide-Area Surveillance

Review on Kernel based Target Tracking for Autonomous Driving

Multicamera tracking of multiple humans based on colored visual hulls

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