Summary of Deep Learning and Hybrid Approaches For Dynamic Scene Analysis, Object Detection and Motion Tracking, by Shahran Rahman Alve
Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking
by Shahran Rahman Alve
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This project aims to develop a robust video surveillance system that segments videos into smaller clips based on the detection of activities. It uses CCTV footage to record only major events, such as the appearance of a person or thief, optimizing storage and making digital searches easier. The system utilizes Convolutional Neural Networks (CNNs) like YOLO, SSD, and Faster R-CNN, Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and other techniques to achieve high accuracy in detection and capture temporal dependencies. It incorporates adaptive background modeling through Gaussian Mixture Models (GMM) and optical flow methods like Lucas-Kanade to detect motions. Multi-scale and contextual analysis are used to improve detection across different object sizes and environments. A hybrid motion segmentation strategy combines statistical and deep learning models to manage complex movements, while optimizations for real-time processing ensure efficient computation. Tracking methods, such as Kalman Filters and Siamese networks, are employed to maintain smooth tracking even in cases of occlusion. Detection is improved on various-sized objects for multiple scenarios by multi-scale and contextual analysis. The results demonstrate high precision and recall in detecting and tracking objects, with significant improvements in processing times and accuracy due to real-time optimizations and illumination-invariant features. This research has the potential to transform video surveillance, reducing storage requirements and enhancing security through reliable and efficient object detection and tracking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The project aims to develop a robust video surveillance system that segments videos into smaller clips based on activity detection. The system uses CCTV footage to record only major events, making it easier to store and search for specific moments. It combines different techniques like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to detect objects accurately and track them over time. The approach also includes adaptive background modeling and optical flow methods to detect motions. To improve detection, the system uses multi-scale and contextual analysis, which helps it work well in different environments and object sizes. The results show that this system can detect and track objects quickly and accurately, making it useful for security and surveillance applications. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Object detection » Optical flow » Precision » Recall » Tracking » Yolo
