A Competitive Neural Network for Multiple Object Tracking in Video Sequence Analysis

Luque‐Baena, Rafael Marcos; Ortiz-de-Lazcano-Lobato, Juan Miguel; López‐Rubio, Ezequiel; Domínguez, Enrique; Palomo, Esteban J.

doi:10.1007/s11063-012-9268-3

Cited by 14 publications

(6 citation statements)

References 20 publications

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“…But their optimization process needs a much more computation storage and time interval than that of other traditional ones, which restricts its direct usages in real-time drug mass production; Marçal [ 7 ] presented new methods based on mathematical morphology and were suitable for grains of circular shape; Kim et al [ 8 ] have successfully proposed a real-time approach for tracking the number of passing people by using one single camera. Similar research founding proposed by Professor Luque-Baena et al can also be learned from [ 9 , 10 ] as well. In the area of moving-objective image detection and its automatic classification, Wong et al [ 11 ] used an example of outdoor people tracking for tourist-flow estimation in a constrained environment.…”

Section: Introductionsupporting

confidence: 84%

Image Tracking for the High Similarity Drug Tablets Based on Light Intensity Reflective Energy and Artificial Neural Network

Liang

Zhou

Liu

et al. 2014

Computational and Mathematical Methods in Medicine

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It is obvious that tablet image tracking exerts a notable influence on the efficiency and reliability of high-speed drug mass production, and, simultaneously, it also emerges as a big difficult problem and targeted focus during production monitoring in recent years, due to the high similarity shape and random position distribution of those objectives to be searched for. For the purpose of tracking tablets accurately in random distribution, through using surface fitting approach and transitional vector determination, the calibrated surface of light intensity reflective energy can be established, describing the shape topology and topography details of objective tablet. On this basis, the mathematical properties of these established surfaces have been proposed, and thereafter artificial neural network (ANN) has been employed for classifying those moving targeted tablets by recognizing their different surface properties; therefore, the instantaneous coordinate positions of those drug tablets on one image frame can then be determined. By repeating identical pattern recognition on the next image frame, the real-time movements of objective tablet templates were successfully tracked in sequence. This paper provides reliable references and new research ideas for the real-time objective tracking in the case of drug production practices.

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Section: Introductionsupporting

confidence: 84%

Image Tracking for the High Similarity Drug Tablets Based on Light Intensity Reflective Energy and Artificial Neural Network

Liang

Zhou

Liu

et al. 2014

Computational and Mathematical Methods in Medicine

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“…Computer vision has been widely developed in terms of target detection and tracking. There exist many excellent tracking algorithms, such as CT [21], STC [22], CSK [23], KCF [11], OCT-KCF [12], and CN [37]. All these algorithms have made contributions in the field of target tracking, but most are sensitive to occlusion in practical environments.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Deep Learning Framework for Occluded Pedestrian Tracking

et al. 2019

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Numerous object-tracking and multiple-person-tracking algorithms have been developed in the field of computer vision, but few trackers can properly address the issue of when a pedestrian is partially or fully occluded by other objects or persons. In order to achieve efficient pedestrian tracking in various occlusion conditions, a pedestrian tracking framework is proposed and developed based on the deep learning networks. First, a pedestrian detector is trained as a tracking mechanism based on the Faster R-CNN, which narrows the search range and efficiently improves accuracy, as compared with the traditional gradient descent algorithm. Second, in the process of target matching, a color histogram and scale-invariant feature transform are combined to provide the target model expression, and a full convolution network (FCN) is trained to extract the pedestrian information in the target model, based on an FCN image semantic segmentation algorithm that can remove background noise effectively. Finally, the extensive experiments on a commonly used tracking benchmark show that the proposed method achieves better performance than the other stateof-the-art trackers in various occlusion situations.

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“…Glowworm swarm optimization (GSO) algorithm is proposed through simulating nature glowworms' behavior [17]. Compared with traditional optimization methods, those swarm intelligence algorithms are more effective and have been utilized to solve complex non-linear optimization problems [13], [17]- [23].…”

Section: Introductionmentioning

confidence: 99%

Improved GSO Algorithms and Their Applications in Multi-Target Detection and Tracking Field

Hou

et al. 2020

IEEE Access

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Fast detection and high-precision tracking of multiple UAVs are the keys to achieving efficient low-altitude defense. However, as there are many hyper-parameters which need to be optimized in target detection network, commonly used methods such as random search method are computationally intensive and cannot quickly obtain multiple optimal hyper-parameters combinations. In addition, angular random walk due to low-frequency noise of speed sensor in servo loop can cause target tracking accuracy to decrease. Fortunately, those two problems can be regarded as a single-mode function optimization problem and a multi-mode function optimization problem, respectively. In the paper, in order to overcome the aforementioned problems, GSOM (glowworm swarm optimization mutation) algorithm and GSOMLDW (glowworm swarm optimization mutation linearly decreasing weight) algorithm are firstly proposed. Furthermore, the global convergence of GSOMLDW has been proven in the paper, which has not been analyzed in currently available literature. Then, experimental results on four multi-modal benchmark functions have strongly illustrated that the novel GSOM algorithm can enhance glowworms' memory ability and improve peak detection rate effectively. When it is used for optimizing hyper-parameters of multi-target detection network, it can be expected to obtain much more hyper-parameter combination selections. Meanwhile, experimental results on ten uni-modal benchmark functions have obviously demonstrated that GSOMLDW algorithm can balance glowworms' exploration and exploitation abilities powerfully and obtain superior global solution accuracy at last. When the GSOMLDW algorithm is used for servo system identification and drift error model identification, the final position error fluctuation after compensation is almost zero while it reaches 1500 urad before compensation. Consequently, the proposed method can effectively improve target tracking precision.INDEX TERMS GSO, system identification, multi-modal functions, multi-target tracking, anti-UAVs.

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A Competitive Neural Network for Multiple Object Tracking in Video Sequence Analysis

Cited by 14 publications

References 20 publications

Image Tracking for the High Similarity Drug Tablets Based on Light Intensity Reflective Energy and Artificial Neural Network

Image Tracking for the High Similarity Drug Tablets Based on Light Intensity Reflective Energy and Artificial Neural Network

An Integrated Deep Learning Framework for Occluded Pedestrian Tracking

Improved GSO Algorithms and Their Applications in Multi-Target Detection and Tracking Field

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