Human Identification System Based on Spatial and Temporal Features in the Video Surveillance System

Sanjeevkumar Angadi and Suvarna Nandyal
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Sanjeevkumar Angadi: Department of Computer Science and Engineering, MIT College of Railway Engineering and Research, Barshi, India & Punyashlok Ahilyadevi Holkar Solapur University, Solapur, India
Suvarna Nandyal: Department of Computer Science and Engineering, Poojya Doddappa Appa College of Engineering, Kalaburagi, India & Visvesvaraya Technological University, Belgavi, India

International Journal of Ambient Computing and Intelligence (IJACI), 2020, vol. 11, issue 3, 1-21

Abstract: Human identification is the most significant topic in the bioinformatics field. Various human gait identification methods are available to identify humans, but detecting the objects based on the human gait is still a challenging task in the video surveillance system. Thus, an effective hybrid Bayesian approach is proposed for identifying the humans. The proposed hybrid Bayesian approach involves two stages as follows: the first stage is the human identification based on the object features, and the second stage is the human identification based on the spatial features. Initially, the videos are fed into the first stage, where the object detection is performed using the Viola Jones algorithm. Once the objects are detected, the feature extraction process is carried out by using a hierarchical skeleton to effectively extract the selective features. The object skeleton provides an effective and intuitive abstraction, which offers object recognition and object matching. The Bayesian network is adapted in the object-based features to identify humans. In the spatial-based human identification stage, only the spatial features are extracted and are passed into the gait-based Bayesian network to identify the humans. Finally, the resulted output is obtained using the fuzzy holoentropy for identifying the humans. The experimentation of the proposed hybrid Bayesian approach is performed using the dataset named UCF-Crime, and the performance is evaluated by considering the average value of the metrics, namely F1-score, precision, and recall which acquired 0.8820, 0.8770, and 0.9203, respectively.

Date: 2020
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