Fast Object Detection Using Multistage Particle Window Deformable Part Model

For object detection, evaluating all sliding windows at various scales draws a computational efficiency issue. In this paper, we propose a fast object detection framework using the multistage particle window strategy to accelerate the cascade deformable part model (DPM). Coupling this strategy with the proposed early jump scheme, adaptive particle window generation, and efficient preprocessing, we demonstrate that the proposed method runs 34.5 times faster than the conventional DPM to detect objects in images, and is able to efficiently detect vehicles and pedestrians in on-road videos.