Multi-Scale and Deformable Object Detection Model Trained with Discriminative Methods

In this documentation, we present a MATLAB implementation of an object detection algorithm based on a discriminatively trained, multi-scale and deformable object detection model. The implementation incorporates key computer vision techniques including histogram of oriented gradients (HOG) feature extraction, deformable part models with latent SVM training, and cascade detection for improved computational efficiency. The algorithm references the following two seminal papers: 1. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010). Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1627-1645. 2. Felzenszwalb, P. F., Girshick, R. B., & McAllester, D. (2010). Cascade object detection with deformable part models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 2241-2248). The code implementation includes several key components: multi-scale image pyramid construction for handling objects at different sizes, deformable part models that allow for flexible object part arrangements, and discriminative training using latent SVM to learn optimal model parameters. The detection pipeline involves computing HOG features across multiple scales, applying learned filters using convolution operations, and performing efficient object localization through dynamic programming that maximizes the scoring function while considering part deformation costs.