Crowd Behavior Recognition Using Dense Trajectories

Khokher, Muhammad Rizwan; Bouzerdoum, A.; Phung, Son Lam

doi:10.1109/dicta.2014.7008098

Cited by 7 publications

(2 citation statements)

References 32 publications

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“…The performance of proposed model is computed in terms of false positive rate, true positive rate and classification accuracy for varied scenarios. Performance of proposed model is compared with state of art techniques which are present in [17] and [18] In Figure 14, a comparative study is presented for considered scenario 1 as mentioned before. This analysis is carried out by computing false positive rate and true positive rate and compared with viscous fluid field method and dense trajectory-based method.…”

Section: Experimental Analysismentioning

confidence: 99%

Crowd Anomaly Detection Using Motion Based Spatio-Temporal Feature Analysis

Basavaraj¹,

Kusagur²

2017

IJEECS

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<p>Recently, the demand for surveillance system is increasing in real time application to enhance the security system. These surveillance systems are mainly used in crowded places such as shopping malls, sports stadium etc. In order to support enhance the security system, crowd behavior analysis has been proven a significant technique which is used for crowd monitoring, visual surveillance etc. For crowd behavior analysis, motion analysis is a crucial task which can be achieved with the help of trajectories and tracking of objects. Various approaches have been proposed for crowd behavior analysis which has limitation for densely crowded scenarios, a new object entering the scene etc. In this work, we propose a new approach for abnormal crowd behavior detection. Proposed approach is a motion based spatio-temporal feature analysis technique which is capable of obtaining trajectories of each detected object. We also present a technique to carry out the evaluation of individual object and group of objects by considering relational descriptors based on their environmental context. Finally, a classification is carried out for detection of abnormal or normal crowd behavior by following patch based process. In the results, we have reported that proposed model is able to achieve better performance when compared to existing techniques in terms of classification accuracy, true positive rate, and false positive rate.</p>

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Section: Experimental Analysismentioning

confidence: 99%

Crowd Anomaly Detection Using Motion Based Spatio-Temporal Feature Analysis

Basavaraj¹,

Kusagur²

2017

IJEECS

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“…As for the problem of activity recognition, multimodal fusion is also popular. [14][15][16][17][18] However, when fusing the RGB and depth channels across different data sets, the channel format must be unified, which is one of the motivations of this work.…”

Section: Introductionmentioning

confidence: 99%

Collecting public RGB-D datasets for human daily activity recognition

Zhang

et al. 2017

International Journal of Advanced Robotic Systems

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Human daily activity recognition has been a hot spot in the field of computer vision for many decades. Despite best efforts, activity recognition in naturally uncontrolled settings remains a challenging problem. Recently, by being able to perceive depth and visual cues simultaneously, RGB-D cameras greatly boost the performance of activity recognition. However, due to some practical difficulties, the publicly available RGB-D data sets are not sufficiently large for benchmarking when considering the diversity of their activities, subjects, and background. This severely affects the applicability of complicated learning-based recognition approaches. To address the issue, this article provides a large-scale RGB-D activity data set by merging five public RGB-D data sets that differ from each other on many aspects such as length of actions, nationality of subjects, or camera angles. This data set comprises 4528 samples depicting 7 action categories (up to 46 subcategories) performed by 74 subjects. To verify the challengeness of the data set, three feature representation methods are evaluated, which are depth motion maps, spatiotemporal depth cuboid similarity feature, and curvature space scale. Results show that the merged large-scale data set is more realistic and challenging and therefore more suitable for benchmarking.

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Real-time crowd behavior recognition in surveillance videos based on deep learning methods

Rezaei

Yazdi

2021

J Real-Time Image Proc

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Crowd Behavior Recognition Using Dense Trajectories

Cited by 7 publications

References 32 publications

Crowd Anomaly Detection Using Motion Based Spatio-Temporal Feature Analysis

Crowd Anomaly Detection Using Motion Based Spatio-Temporal Feature Analysis

Collecting public RGB-D datasets for human daily activity recognition

Real-time crowd behavior recognition in surveillance videos based on deep learning methods

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