The VOC-DPM repository is an implementation of an object detection system built on deformable part models (DPMs) and latent SVM learning, specifically packaged as “voc-release5.” It is the companion code for Ross Girshick’s dissertation, and extends earlier work on discriminatively trained DPMs. The system supports a grammar-based representation for object models, allowing structures such as mixtures and hierarchies to represent parts and whole objects. It implements both latent SVM training (where part assignments are treated as latent variables) and weak-label structural SVM (WL-SSVM) for learning from partially labeled data. The code integrates several enhancements: a star-cascade detection algorithm to speed up screening, context rescoring (re-ranking detections using contextual information), and various optimizations like in-memory training (rather than large on-disk data files).
Features
- Implements deformable part-based models for object detection
- Utilizes latent SVM learning for part localization
- Includes code and models compatible with PASCAL VOC datasets (VOC2007, VOC2010)
- Comes with demo scripts (demo.m, demo_cascade.m) and training utilities
- Parallelizable MATLAB implementation with OpenMP support
- Tested on Linux and macOS with MATLAB R2008b and newer