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AI-Enhanced Tracksegnet an Advanced Machine Learning Technique for Video Segmentation and Object Tracking


Affiliations
1 Department of Information Technology, Institute of Technology and Management, India
2 Department of Biomedical Engineering, Government Polytechnic, Gandhinagar, India
3 Department of Computer Science and Engineering, Jodhpur Institute of Engineering and Technology, India
4 Department of Computer Science and Applications, ITM University, India
5 Department of Computer Science and Applications, Jiwaji University, India
6 Department of Computer Science, College of Computer Science, King Khalid University, Saudi Arabia

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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.

Keywords

Video Segmentation, Object Tracking, Deep Learning, Attention Mechanisms, Convolutional Neural Networks
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Abstract Views: 46




  • AI-Enhanced Tracksegnet an Advanced Machine Learning Technique for Video Segmentation and Object Tracking

Abstract Views: 46  | 

Authors

Jitendra Singh Kushwah
Department of Information Technology, Institute of Technology and Management, India
Maitriben Harshadbhai Dave
Department of Biomedical Engineering, Government Polytechnic, Gandhinagar, India
Ankita Sharma
Department of Computer Science and Engineering, Jodhpur Institute of Engineering and Technology, India
Keerti Shrivastava
Department of Computer Science and Applications, ITM University, India
Rajeev Sharma
Department of Computer Science and Applications, Jiwaji University, India
Mohammad Nadeem Ahmed
Department of Computer Science, College of Computer Science, King Khalid University, Saudi Arabia

Abstract


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.

Keywords


Video Segmentation, Object Tracking, Deep Learning, Attention Mechanisms, Convolutional Neural Networks