Discriminatively trained particle filters for complex multi-object tracking

Hess, Rob; Fern, Alan

doi:10.1109/cvpr.2009.5206801

Cited by 97 publications

(61 citation statements)

References 12 publications

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“…The results including the comparison with the method in [9] for pedestrian 1 and pedestrian 2 are demonstrated in Fig. 6 and Fig.…”

Section: Tracking Precision Testmentioning

confidence: 99%

“…11, where the green and red curves are corresponding to the method in [9] and our proposed method, respectively. The results shows that our method has lower error than the method in [9]. Figure 12 depicts the result where a pedestrian moves through 3 different scenes.…”

Section: Object Re-identification Performancementioning

confidence: 99%

“…Positioning and iterative tracking of moving object are implemented based on the cycle process of "prediction and update" [8], [9]. In practical use, the tracking precision is decreased because the feature of object is changing over time.…”

Section: Object Tracking Based On An Improved Particle Filtermentioning

confidence: 99%

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Multiple-Object Tracking in Large-Scale Scene

Yuan

Cao

Tan

et al. 2016

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, a multiple-object tracking approach in largescale scene is proposed based on visual sensor network. Firstly, the object detection is carried out by extracting the HOG features. Then, object tracking is performed based on an improved particle filter method. On the one hand, a kind of temporal and spatial dynamic model is designed to improve the tracking precision. On the other hand, the cumulative error generated from evaluating particles is eliminated through an appearance model. In addition, losses of the tracking will be incurred for several reasons, such as occlusion, scene switching and leaving. When the object is in the scene under monitoring by visual sensor network again, object tracking will continue through object re-identification. Finally, continuous multiple-object tracking in large-scale scene is implemented. A database is established by collecting data through the visual sensor network. Then the performances of object tracking and object re-identification are tested. The effectiveness of the proposed multiple-object tracking approach is verified.

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“…The results including the comparison with the method in [9] for pedestrian 1 and pedestrian 2 are demonstrated in Fig. 6 and Fig.…”

Section: Tracking Precision Testmentioning

confidence: 99%

Section: Object Re-identification Performancementioning

confidence: 99%

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Multiple-Object Tracking in Large-Scale Scene

Yuan

Cao

Tan

et al. 2016

IEICE Trans. Inf. & Syst.

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“…However, to speed up the process of tracking and to accommodate the non-Gaussianness nature of the problem, a group of sequential Monte Carlo methods, also known as Particle Filters, is utilized (Rothrock and Drummond, 2000, Danescu et al, 2009, Hue et al, 2002. Particle filters can be discriminatively trained for specific environment and different objectsto-be-tracked tasks, as demonstrated by Hess and Fern in (Hess and Fern, 2009). Current approaches in vehicle tracking from aerial or satellite imagery aim at off-line optimization of data association, e.g.…”

Section: Related Workmentioning

confidence: 99%

Automatic Vehicle Trajectory Extraction for Traffic Analysis From Aerial Video Data

Apeltauer

Babinec²,

Herman³

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents a new approach to simultaneous detection and tracking of vehicles moving through an intersection in aerial images acquired by an unmanned aerial vehicle (UAV). Detailed analysis of spatial and temporal utilization of an intersection is an important step for its design evaluation and further traffic inspection. Traffic flow at intersections is typically very dynamic and requires continuous and accurate monitoring systems. Conventional traffic surveillance relies on a set of fixed cameras or other detectors, requiring a high density of the said devices in order to monitor the intersection in its entirety and to provide data in sufficient quality. Alternatively, a UAV can be converted to a very agile and responsive mobile sensing platform for data collection from such large scenes. However, manual vehicle annotation in aerial images would involve tremendous effort. In this paper, the proposed combination of vehicle detection and tracking aims to tackle the problem of automatic traffic analysis at an intersection from visual data. The presented method has been evaluated in several real-life scenarios.

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“…camera motion, illumination changes, and object resolution). Extensive work has been done over the years [1,2], as it is a very complicated and challenging problem. In this paper, we address the problem of robust multi-target tracking within sports videos (e.g.…”

Section: Introductionmentioning

confidence: 99%

Robust multi-object tracking via cross-domain contextual information for sports video analysis

Zhang

Ghanem

Ahuja

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Multiple player tracking is one of the main building blocks needed in a sports video analysis system. In an uncalibrated camera setting, robust mutli-object tracking can be very difficult due to a number of reasons including the presence of noise, occlusion, fast camera motion, low-resolution image capture, varying viewpoints and illumination changes. To address the problem of multi-object tracking in sports videos, we go beyond the video frame domain and make use of information in a homography transform domain that is denoted the homography field domain. We propose a novel particle filter based tracking algorithm that uses both object appearance information (e.g. color and shape) in the image domain and cross-domain contextual information in the field domain to improve object tracking. In the field domain, the effect of fast camera motion is significantly alleviated since the underlying homography transform from each frame to the field domain can be accurately estimated. We use contextual trajectory information (intra-trajectory and inter-trajectory context) to further improve the prediction of object states within an particle filter framework. Here, intra-trajectory contextual information is based on history tracking results in the field domain, while inter-trajectory contextual information is extracted from a compiled trajectory dataset based on tracks computed from videos depicting the same sport. Experimental results on real world sports data show that our system is able to effectively and robustly track a variable number of targets regardless of background clutter, camera motion and frequent mutual occlusion between targets.

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Discriminatively trained particle filters for complex multi-object tracking

Cited by 97 publications

References 12 publications

Multiple-Object Tracking in Large-Scale Scene

Multiple-Object Tracking in Large-Scale Scene

Automatic Vehicle Trajectory Extraction for Traffic Analysis From Aerial Video Data

Robust multi-object tracking via cross-domain contextual information for sports video analysis

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