Low-resolution vehicle tracking using dense and reduced local gradient features maps
Michael P. Dessauer, Sumeet Dua · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2010
We present a novel method to quickly detect and track objects of low resolution within an image frame by comparing dense, oriented gradient features at multiple scales within an object chip. The proposed method uses vector correlation between sets of oriented Haar filter responses from within a local window and an object library to create similarity measures, where peaks indicate high object probability. Interest points are chosen based on object shape and size so that each point represents both a distinct spatial location and the shape segment of the object. Each interest point is then independently searched in subsequent frames, where multiple similarity maps are fused to create a single object probability map. This method executes in real time by reducing feature calculations and approximations using box filters and integral images. We achieve invariance to rotation and illumination, because we calculate interest point orientation and normalize the feature vector scale. The method creates a feature set from a small and localized area, allowing for accurate detections in low resolution scenarios. This approach can also be extended to include the detection of partially occluded objects through calculating individual interest point feature vector correlations and clustering points together. We have tested the method on a subset of the Columbus Large Image Format (CLIF) 2007 dataset, which provides various low-pixel-on-object moving and stationary vehicles with varying operating conditions. This method provides accurate results with minimal parameter tuning for robust implementation on aerial, low pixel-on-object data sets for automated classification applications.