Vehicle Detection Method Using Haar Features and SRC (Sparse Representation Classification) with Implementation Code
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Resource Overview
Implementation of a vehicle detection method using Haar features combined with SRC (Sparse Representation Classification). The provided files include training and test vehicle images. Note: The Haar features haven't been optimized due to time constraints, resulting in high dimensionality and slow sliding window processing. The code outputs performance statistics for reference, demonstrating sparse representation applications in vehicle detection. Key implementation aspects include feature extraction using Haar-like features and classification via sparse coding optimization algorithms.
Detailed Documentation
This paper presents a vehicle detection method implemented using Haar features combined with SRC (Sparse Representation Classification). The provided files include both training and test vehicle images for practical usage. Due to time constraints, the Haar features used in this implementation haven't undergone optimization, leading to high dimensionality and consequently slower processing speed in the sliding window approach. However, the code provides comprehensive performance statistics, serving as a valuable reference for understanding how sparse representation techniques can be applied in vehicle detection systems. The implementation typically involves extracting Haar-like features through integral image computation and performing classification using l1-norm minimization algorithms for sparse coding. We hope this resource proves beneficial for learning and research purposes.
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