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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Sriram, Kusuma
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Graph neural network based human detection in videos during occlusion environments Sriram, Kusuma; Purushotham, Kiran
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp2616-2624

Abstract

One of the most difficult perceptual problems for many applications is accurately recognizing the human object in a variety of circumstances. This can be difficult due to obstructions, weather, complex backdrops, cast shadows, and occlusions. Occlusion is a challenging open problem where a detector can only perceive a portion of the target human because of obstacles in the surrounding. In this research, an experimental investigation was conducted using the multi object tracking (MOT17) datasets to construct a graph neural network-based solution for the detection of humans in videos while considering the possibility of occlusion. Graph neural network (GNN) is used for the construction of neural solver model for detecting human object in occlusion scenario. The results obtained shows that this proposed method offers a considerable improvement in efficiency in comparison to the ways that have been used in the past. The values obtained for the standard performance metrics are higher than the state-of-the-art methods.
Enhancing video anomaly detection for human suspicious behavior through deep hybrid temporal spatial network Sriram, Kusuma; Purushotham, Kiran
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4121-4128

Abstract

Abnormal behavior exhibited by individuals with particular intentions is common, and when such behavior occurs in public places, it can cause physical and mental harm to others. Considering the rise in the automated approach for anomaly detection in videos, accuracy becomes essential. Most existing models follow a deep learning architecture, which faces challenges due to variations in motion. This research work develops a deep learning based hybrid architecture with temporal and spatial features. The hybrid temporal spatial network (HTSNet) consists of two customized architectures: a graph neural network (GNN) and a convolutional neural network (CNN). HTSNet combined with a novel classifier to extract features and classify normal and abnormal behavior. The performance of HTSNet is rigorously evaluated using the University of California, San Diego-Pedestrian 1 (UCSD Ped1) dataset, a benchmark in computer vision research for anomaly detection in video surveillance. The effectiveness of HTSNet is demonstrated through a comparative analysis with current state-of-the-art methods, using the area under the curve (AUC) metric as a standard measure of performance. This paper contributes to the advancement of video surveillance technology, providing a robust framework for enhancing public safety and security in an increasingly digital world.
Co-Authors Purushotham, Kiran