Object Tracking and Identification with OpenCV Using Histograms.pptx

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1. Object Tracking and Identification with OpenCV Using Histograms and SVM By Suriya C N( 21121007) Tharun K(21121043)

2. Abstract Object tracking and identification are critical components of computer vision systems, with applications spanning surveillance, robotics, autonomous vehicles, and human-computer interaction. This project presents an efficient approach for real-time object tracking and identification using OpenCV, histograms, and Support Vector Machine (SVM). The proposed system employs the Histogram of Oriented Gradients (HOG) to extract robust feature representations from object images. These features are then used to train an SVM classifier for accurate object identification. OpenCV’s optimized libraries facilitate real-time implementation, enabling effective tracking and classification in video streams. The system is tested on benchmark datasets and customized environments to validate its performance in varying lighting conditions, object orientations, and cluttered backgrounds. Experimental results demonstrate high detection accuracy and reliable tracking, even on resource-constrained platforms like the Raspberry Pi. This project showcases the potential of combining lightweight algorithms with hardware efficiency, making it suitable for low-power, edge-computing IoT applications. Future work includes extending the system for multi-object tracking and integrating deep learning models for enhanced generalization.

3. Introduction Object tracking and identification play a pivotal role in computer vision, with applications in surveillance, robotics, and autonomous systems. Real-time performance and accuracy are critical, especially in dynamic environments. While deep learning approaches like CNNs provide high accuracy, their computational demands make them unsuitable for resource-constrained devices. This project presents a lightweight solution using OpenCV, histograms, and Support Vector Machines (SVMs) for real-time object tracking and identification. Histogram of Oriented Gradients (HOG) is utilized for feature extraction, providing robust shape-based features, while an SVM classifier ensures accurate object recognition. The system is implemented on a Raspberry Pi, leveraging OpenCV’s optimizations to achieve real-time performance suitable for IoT and edge computing. Experimental results validate the system’s effectiveness in challenging scenarios, demonstrating its feasibility as a low-power, cost-effective solution.

4. Research Context and Motivation Object tracking and identification are essential in applications like surveillance and autonomous systems, where challenges such as occlusions, varying lighting, and dynamic environments must be addressed. While traditional methods often fail in real-time scenarios, recent advancements in machine learning, particularly Support Vector Machines (SVMs) combined with classical computer vision techniques, offer promising solutions that are computationally feasible for low-power devices. Existing systems using HOG and SVM for object detection are effective but typically rely on high-performance hardware. This project aims to bridge this gap by implementing an efficient real-time tracking system on a Raspberry Pi, a resource-constrained platform ideal for IoT and edge computing applications. By utilizing HOG for feature extraction and SVM for classification, this solution offers a lightweight, low-complexity approach to real-time object tracking. Additionally, the integration of OpenCV optimizations will further enhance system performance, enabling deployment in real-world scenarios with limited computational resources.

5. Objectives Develop an efficient and real-time object tracking and identification system using OpenCV, HOG, and SVM on the Raspberry Pi. Apply Histogram of Oriented Gradients (HOG) for robust feature extraction, capturing shape-based characteristics of objects. Utilize Support Vector Machines (SVM) for classifying objects based on the extracted HOG features, ensuring reliable and accurate identification. Optimize the system for execution on low-power devices, such as the Raspberry Pi, using OpenCV’s optimized libraries to achieve real-time performance. Test and validate the system under various real-world conditions, including dynamic backgrounds, occlusions, and lighting variations, to ensure robustness and reliability.

6. Module Split-up Module 1: Hardware Setup:Configure the Raspberry Pi and camera for video input. Install required software (OpenCV, Python). Module 2: HOG Feature Extraction:Implement the HOG algorithm to extract shape-based features from video frames. Module 3: SVM Object Classification:Train and integrate an SVM model for object identification using extracted HOG features. Module 4: Object Tracking:Develop a real-time tracking algorithm to follow identified objects across frames. Module 5: System Optimization:Optimize the system for real-time performance on the Raspberry Pi using OpenCV’s efficient functions. Module 6: Testing and Validation:Test the system in various conditions and evaluate its performance (accuracy and processing time). Module 7: User Interface:Create a simple interface for real-time display and system control