Unusual Crowd Activity Detection using OpenCV and Motion Influence Map

Jhapate, Arun Kumar; Malviya, Sunil; Jhapate, Monika

doi:10.1109/idea49133.2020.9170704

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…The experiments and proposed deep learning framework were carried out using a single NVIDIA graphics processing unit (GPU) and an Intel Core i7 3.4GHz processor with 32GB random-access memory (RAM) and a 32GB NVIDIA graphics card, all of which were configured using CUDA-optimized architecture and the open source computer vision library (OpenCV) deep learning framework. The suggested method is compared to the state-of-the-art unusual activity detection methods [29], [30], social force models [31], sparse representation-based method [32], and mixture of dynamic textures-based method [32]. True positive (TP), true negative (TN), false positive (FP), false negative (FN), equal error rate (ERR), true positive rate (TPR), true negative rate (TNR), and area under curve (AUC) are some common performance measuring metrics.…”

Section: Resultsmentioning

confidence: 99%

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Novel approach for pedestrian unusual activity detection in academic environment

Hajari

Gawande

Golhar

2022

IJ-AI

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<span>In this paper, we propose an efficient method for the detection of student unusual activity in the academic environment. The proposed method extracts motion features that accurately describe the motion characteristics of the pedestrian's movement, velocity, and direction, as well as their intercommunication within a frame. We also use these motion features to detect both global and local anomalous behaviors within the frame. The proposed approach is validated on a newly built proposed student behavior database and three additional publicly available benchmark datasets. When compared to state-of-the-art techniques, the experimental results reveal a considerable performance improvement in anomalous activity recognition. Finally, we summarize and discuss future research directions.</span>

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Section: Resultsmentioning

confidence: 99%

“…First, we have performed experiments on University of Minnesota anomaly detection dataset. Figure 3 shows the receiving operating curve (ROC) for the proposed and existing approach presented in [31], [32]. Similarly, we have computed the ROC for the proposed student behavior dataset in Figure 4.…”

Section: Resultsmentioning

confidence: 99%

Novel approach for pedestrian unusual activity detection in academic environment

Hajari

Gawande

Golhar

2022

IJ-AI

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“…The proposed technique was used to compare the social force model of [58], sparse representation of [55], and mixture of dynamic textures method of [40], [59], [60]. The performance is assessed using standard performance evaluation metrics.…”

Section: Suspicious Activity Recognitionmentioning

confidence: 99%

“…First, we ran tests using the dataset for anomaly detection from the University of Minnesota. The Receiving Operating Curve (ROC) for the proposed and current technique described in [55] is shown in Fig. 15.…”

Section: Suspicious Activity Recognitionmentioning

confidence: 99%

Novel Pedestrian Detection and Suspicious Activity Recognition Using Enhanced YOLOv5 and Motion Feature Map

Gawande¹,

Hajari²,

Golhar

2023

Preprint

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Pedestrian detection and suspicious activity recognition are notable challenges in vision-based surveillance systems. However, the accuracy of pedestrian detection is influenced by a wide range of factors, including human presence, trajectory, posture, complex background, and object deformation. In this paper, we developed a pedestrian dataset that encompasses student behavior in an institution, such as cheating, stealing lab devices, dispute, and threatening scenarios. It provides uniform and steady identification annotations of pedestrians, making it suitable for pedestrian detection, tracking, and behavior detection. In addition, we also improved detection accuracy using the enhanced YOLOv5 architecture. Next, we proposed an efficient method for detection of global and local unusual behaviors. The proposed method extracts motion features that accurately describe pedestrian motion, speed, and direction, etc. The proposed method is validated on a proposed student behavior database and three additional publicly available benchmark datasets. It gives state-of-the-art detection accuracy of 96.12% and miss rate of 6.68% compared to the existing techniques. The experimental results demonstrate a substantial performance improvement in anomalous activity recognition. The final section summarizes and discusses future research directions.

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