A Robust Tracking System for Low Frame Rate Video

Zhang, Xiaoqin; Hu, Weiming; Xie, Nianhua; Bao, Hujun; Maybank, Stephen J.

doi:10.1007/s11263-015-0819-8

Cited by 77 publications

(14 citation statements)

References 49 publications

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“…In order to solve this problem, referring to the information exchanging strategy of individuals in the particle swarm optimization (PSO) algorithm [22,23], the flipping variable Δ(i) in the BFO algorithm is updated in (11) and shown in Figure 4:…”

Section: Improvement Of Chemotaxis Flipping Based On Information Exchmentioning

confidence: 99%

Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy

Chen

Wang

et al. 2020

Computational Intelligence and Neuroscience

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Bacterial foraging optimization (BFO) algorithm is a novel swarm intelligence optimization algorithm that has been adopted in a wide range of applications. However, at present, the classical BFO algorithm still has two major drawbacks: one is the fixed step size that makes it difficult to balance exploration and exploitation abilities; the other is the weak connection among the bacteria that takes the risk of getting to the local optimum instead of the global optimum. To overcome these two drawbacks of the classical BFO, the BFO based on self-adaptive chemotaxis strategy (SCBFO) is proposed in this paper. In the SCBFO algorithm, the self-adaptive chemotaxis strategy is designed considering two aspects: the self-adaptive swimming based on bacterial search state features and the improvement of chemotaxis flipping based on information exchange strategy. The optimization results of the SCBFO algorithm are analyzed with the CEC 2015 benchmark test set and compared with the results of the classical and other improved BFO algorithms. Through the test and comparison, the SCBFO algorithm proves to be effective in reducing the risk of local convergence, balancing the exploration and the exploitation, and enhancing the stability of the algorithm. Hence, the major contribution in this research is the SCBFO algorithm that provides a novel and practical strategy to deal with more complex optimization tasks.

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Section: Improvement Of Chemotaxis Flipping Based On Information Exchmentioning

confidence: 99%

Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy

Chen

Wang

et al. 2020

Computational Intelligence and Neuroscience

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“…In videos with high frame rates, Dollar et al [79] extracted features in pyramidal structure in order to fast detect moving objects. Recently, in [80], a tracking system for low frame rate videos was proposed which is used a dominant color based appearance model and the APSO based framework search to track moving objects. When we deal with low resolution video sequences, the techniques based on fusion can be used to better detect moving objects.…”

Section: Problems Related To Cameramentioning

confidence: 99%

New trends on moving object detection in video images captured by a moving camera: A survey

Yazdi

Bouwmans

2018

Computer Science Review

207

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This paper presents a survey on the latest methods of moving object detection in video sequences captured by a moving camera. Although many researches and excellent works have reviewed the methods of object detection and background subtraction for a fixed camera, there is no survey which presents a complete review of the existing different methods in the case of moving camera. Most methods in this field can be classified into four categories: modelling based background subtraction, trajectory classification, low rank and sparse matrix decomposition, and object tracking. We discuss in details each category and present the main methods which proposed improvements in the general concept of the techniques. We also present challenges and main concerns in this field as well as performance metrics and some benchmark databases available to evaluate the performance of different moving object detection algorithms.

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“…Inspired by [20,28,31], we propose four types of co-occurrence relations to represent the interaction among the nodes in our hierarchical model. These four types of co-occurrence relations are block-block dependencies (in layer 3), block-part dependencies (between layers 3 and 2), part-part dependencies (in layer 2), and part-subspace dependencies (between layers 2 and 1), as shown in Fig.…”

Section: Representation Of the Hierarchical Co-occurrence Modelmentioning

confidence: 99%

Pedestrian detection based on hierarchical co-occurrence model for occlusion handling

Zhang

Jiang

et al. 2015

Neurocomputing

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a b s t r a c tIn pedestrian detection, occlusions are typically treated as an unstructured source of noise and explicit models have lagged behind those for object appearance, which will result in degradation of detection performance. In this paper, a hierarchical co-occurrence model is proposed to enhance the semantic representation of a pedestrian. In our proposed hierarchical model, a latent SVM structure is employed to model the spatial co-occurrence relations among the parent-child pairs of nodes as hidden variables for handling the partial occlusions. Moreover, the visibility statuses of the pedestrian can be generated by learning co-occurrence relations from the positive training data with large numbers of synthetically occluded instances. Finally, based on the proposed hierarchical co-occurrence model, a pedestrian detection algorithm is implemented to incorporate visibility statuses by means of a Random Forest ensemble. The experimental results on three public datasets demonstrate the log-average miss rate of the proposed algorithm has 5% improvement for pedestrians with partial occlusions compared with the state-of-the-arts.

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A Robust Tracking System for Low Frame Rate Video

Cited by 77 publications

References 49 publications

Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy

Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy

New trends on moving object detection in video images captured by a moving camera: A survey

Pedestrian detection based on hierarchical co-occurrence model for occlusion handling

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