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

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A cost-effective, reliable and accurate framework for multiple-target tracking by detection approach using deep neural network Divyaprabha, Divyaprabha; Seebaiah, Guruprsad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5681-5690

Abstract

Over the years the area of object tracking and detection has emerged and become ubiquitous owing to its potential contribution towards video surveillance applications. Multiple object tracking (MOT) estimates the trajectory of several objects of interest simultaneously over time in a series of video frames. Even though various research proposals have encouraged the use of machine learning techniques in designing multi-object trackers, the existing solutions need to be more practicable for online tracking due to more complicated algorithms, The study, therefore, introduces a cost-effective tracking solution for multiple–target tracking by detection where it incorporates the you only look once version 4 (YOLOv4) and person re-identification network, which are further integrated with the proposed tracking model, which considers both bounding box and appearance features to handle the motion prediction and data association respectively. The novelty of this approach lies in considering appearance features, which not only help predict tracks through allocations problem solving but also handle the cost of computation problems. Here, the system utilizes a pre-trained association metric with which the occlusion challenges are also handled, whereas the target tracking has taken place even in more extended periods of occlusion, making it suitable with the existing efficient tracking algorithms.
Framework for abnormal event detection and tracking based on effective sparse factorization strategy Divyaprabha, Divyaprabha; Seebaiah, Guruprasad
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.pp3900-3908

Abstract

The idea of tracking video objects has evolved to facilitate the area of surveillance systems. However, most current research efforts lie in speedy abnormal event detection and tracking of objects of interest tracking. However, the primary challenge is dealing with complex video structures' inherent redundancy. The existing research models for video tracking are more inclined towards improving accuracy. In contrast, the consideration of a more significant proportion of mobile object dynamics, e.g. abnormal events, in motion over the crowded video frame sequence is mainly overlooked, which is essential to study a specific movement pattern of the object of interest appearing in the frame sequence concerning the cost of computation factors. The study thereby introduces a unique strategy of speedy abnormal event detection and tracking, which facilitates video tracking to assess a specific pattern of object of interest movement over complex and crowded video scenes, considering a unique learning-based approach. The extensive simulation outcome further shows that the proposed tracking model accomplishes better tracking accuracy yet retains an optimized computation cost compared to the baseline studies. The computation of video tracking also accomplishes higher detection rates even in the challenging constraints of partial/complete occlusion, illumination variation and background clutter.
Co-Authors Seebaiah, Guruprasad Seebaiah, Guruprsad