Automatic multi-vehicle tracking using video cameras: An improved CAMShift approach

Xia, Jingxin; Rao, Wenming; Huang, Wei; Lu, Zhenbo

doi:10.1007/s12205-013-0263-7

Cited by 13 publications

(6 citation statements)

References 10 publications

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“…The Cam-shift algorithm is easy to converge to the non-target area when tracking the color changes and large area color interference, but the background is too complex to converge to the target area, which would lead to the failure of object tracking [14][15][16] . The STC learning algorithm can efficiently use information between frames and local context information of the target to calculate and estimate target position of the likelihood confidence map by considering the local context background information of target area.…”

Section: Introductionmentioning

confidence: 99%

Automatic monitoring method of cow ruminant behavior based on spatio-temporal context learning

Chen¹,

He²,

Song³

2018

International Journal of Agricultural and Biological Engineering

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Automatic monitoring of cow rumination has great significance in the development of modern animal husbandry. In order to solve the problem of high real-time requirement of ruminant behavior monitoring, a tracking method based on STC (Spatio-Temporal Context) learning was carried out. On the basis of cow's mouth region extraction, the spatial context model between target object and its local surrounding background was built based on their spatial correlations by solving the deconvolution problem, and the learned spatial context model was used to update the STC learning model for the next frame. Tracking in the next frame was formulated by computing a confidence map as a convolution problem that integrates the STC learning information, and the best object location could be estimated by maximizing the confidence map. Then the target scale was estimated based on the confidence evaluation. Finally, accurate tracking of the mouth movement trajectory was realized. To verify the effectiveness of the proposed method, the performance of the algorithm was tested using 20 video sequences. Besides, the tracking results were compared with the Mean-shift algorithm. The results showed that the average success rate of STC learning monitoring algorithm was 85.45%, which was 9.45% higher than the Mean-shift algorithm, the detection rate of STC learning monitoring algorithm was 18.56 s per video, which was 22.08% higher than that of the Mean-shift algorithm. The results showed that the fast tracking method based on STC learning monitoring algorithm is effective and feasible.

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Section: Introductionmentioning

confidence: 99%

Automatic monitoring method of cow ruminant behavior based on spatio-temporal context learning

Chen¹,

He²,

Song³

2018

International Journal of Agricultural and Biological Engineering

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“…Among specialized approaches, the works of Xia et al [24] focus on wide-area traffic monitoring for highway roads. Odobez et al [9] designed a metro station monitoring system that aims at automatically detecting dangerous situations which may lead to accidents or violence.…”

Section: Surveillance Systemsmentioning

confidence: 99%

Smart surveillance framework: A versatile tool for video analysis

Nazare

Santos

Ferreira

et al. 2014

IEEE Winter Conference on Applications of Computer Vision

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Computer Vision problems applied to visual surveillance have been studied for several years aiming at finding accurate and efficient solutions, required to allow the execution of surveillance systems in real environments. The main goal of such systems is to analyze the scene focusing on the detection and recognition of suspicious activities performed by humans in the scene, so that the security personnel can pay closer attention to these preselected activities. To accomplish that, several problems have to be solved first, for instance background subtraction, person detection, tracking and re-identification, face recognition, and action recognition. Even though each of these problems have been researched in the past decades, they are hardly considered in a sequence, each one is usually solved individually. However, in a real surveillance scenarios, the aforementioned problems have to be solved in sequence considering only videos as the input. Aiming at the direction of evaluating approaches in more realistic scenarios, this work proposes a framework called Smart Surveillance Framework (SSF), to allow researchers to implement their solutions to the above problems as a sequence of processing modules that communicate through a shared memory.

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“…Based on the weighted color histogram [5,6], the histogram back-projection operation is applied for calculating the color probability distribution. By replacing the pixel values in the raw image with the probability, a gray image (the color probability distribution image) can be obtained.…”

Section: Mean Shift Algorithm In Tracking Of Moving Objectmentioning

confidence: 99%

The optimization of improved mean shift object tracking in embedded multicore DSP parallel system

et al. 2014

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This paper proposes a more robust and efficient Mean Shift object tracking algorithm which is optimized for embedded multicore DSP Parallel system. Firstly, the RGB image is transformed into HSV image which is robust in many aspects such as lighting changes. Then, the color histogram model is used in the back projection process to generate the color probability distribution. Secondly, the size and position of search window are initialized in the first frame, and Mean Shift algorithm calculates the center position of the target and adjusts the search window automatically both in size and location, according to the result of the previous frame. Finally, since the multicore DSP system is commonly adopted in the embedded application such as seeker and an optical scout system, we implement the proposed algorithm in the TI multicore DSP system to meet the need of large amount computation. For multicore parallel computing, the explicit IPC based multicore framework is designed which outperforms OpenMP standard. Moreover, the parallelisms of 8 functional units and cross path data fetch capability of C66 core are utilized to accelerate the computation of iteration in Mean Shift algorithm. The experimental results show that the algorithm has good performance in complex scenes such as deformation, scale change and occlusion, simultaneously the proposed optimization method can significantly reduce the computation time.

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Automatic multi-vehicle tracking using video cameras: An improved CAMShift approach

Cited by 13 publications

References 10 publications

Automatic monitoring method of cow ruminant behavior based on spatio-temporal context learning

Automatic monitoring method of cow ruminant behavior based on spatio-temporal context learning

Smart surveillance framework: A versatile tool for video analysis

The optimization of improved mean shift object tracking in embedded multicore DSP parallel system

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