In this work we present a new part-based object detection algorithm with hundreds of parts performing real-time detection. Part-based models are currently state-of-the-art for object detection due to their ability to represent large appearance variations. However, due to their high computational demands such methods are limited to several parts only and are too slow for practical real-time implementation. Our algorithm is an accelerated version of the "Feature Synthesis'' (FS) method[1], which uses multiple object parts for detection and is among state-of-the-art methods on human detection benchmarks, but also suffers from a high computational cost. The proposed Accelerated Feature Synthesis (AFS) uses several strategies for reducing the number of locations searched for each part. The first strategy uses a novel algorithm for approximate nearest neighbor search which we developed, termed "KD-Ferns'', to compare each image location to only a subset of the model parts. Candidate part locations for a specific part are further reduced using spatial inhibition, and using an object-level "coarse-to-fine'' strategy. In our empirical evaluation on pedestrian detection benchmarks, AFS maintains almost fully the accuracy performance of the original FS, while running more than X4 faster than existing part-based methods which use only several parts. AFS is to our best knowledge the first part-based object detection method achieving real-time running performance: nearly 10 frames per-second on 640X480 images on a regular CPU.
['Dan Levi', 'Shai Silberstein', 'Aharon Bar-Hillel']
Fast Multiple-Part Based Object Detection Using KD-Ferns