Video segmentation and object tracking are critical tasks in computer
vision with applications spanning surveillance, autonomous driving,
and interactive media. Traditional methods often struggle with the
dynamic nature of video data, where object occlusions, variations in
illumination, and complex motion patterns present significant
challenges. Existing segmentation and tracking systems frequently
suffer from inaccuracies in handling real-time video sequences,
particularly in distinguishing and tracking multiple overlapping
objects. The limitations of current models in addressing these issues
necessitate the development of more advanced techniques that can
effectively manage dynamic scenes and improve tracking accuracy. To
address these challenges, we propose an advanced machine learning
technique, AI-Enhanced TrackSegNet, which integrates deep learning
with novel attention mechanisms for improved video segmentation and
object tracking. Our method utilizes a combination of Convolutional
Neural Networks (CNNs) for feature extraction and Long Short-Term
Memory (LSTM) networks for temporal sequence modeling. We
introduce an attention-based mechanism to dynamically focus on
relevant features, enhancing the model's ability to handle occlusions
and varying object appearances. The model was trained on a diverse
dataset of video sequences, incorporating both synthetic and real-world
footage. The AI-Enhanced TrackSegNet demonstrated significant
improvements in performance compared to existing techniques. Our
method achieved an average Intersection over Union (IoU) score of
86.7% for segmentation and a tracking precision rate of 91.3% on the
MOT17 benchmark dataset. These results represent a 10.2%
improvement in IoU and a 7.5% increase in tracking precision
compared to state-of-the-art methods. The model also exhibited
enhanced robustness in complex scenes, handling occlusions and
motion variations with greater accuracy.
Jitendra Singh Kushwah1, Maitriben Harshadbhai Dave2, Ankita Sharma3, Keerti Shrivastava4, Rajeev Sharma5, Mohammad Nadeem Ahmed6
Institute of Technology and Management, India1, Government Polytechnic, Gandhinagar, India2, Jodhpur Institute of Engineering and Technology, India3, ITM University, India4, Jiwaji University, India5, King Khalid University, Saudi Arabia6
Video Segmentation, Object Tracking, Deep Learning, Attention Mechanisms, Convolutional Neural Networks
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 1 , Pages: 3384 - 3394 )
Date of Publication :
August 2024
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88
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