Mechanical Particle Filter-Based Active Vision System for Fast Wide-Area Multiobject Detection

Abstract

Sliding window searching is a common paradigm employed for large Field-of-View (FOV) multi-object detection. However, low target resolution and irregular distribution of objects make the traditional method inefficient and hard to meet the demands of fast object detection in a wide area. To address the above issues, in this paper we design an ultrafast switching galvano-mirror based active vision system that can simultaneously search and detect multi-object at 300 frames per second (fps) in a large FOV, without the guidance of a high-resolution (HR) panoramic camera. Specifically, a novel mechanical particle filter (MPF) framework is proposed to improve the efficiency of searching potential objects by constructing the probability distribution model and then iteratively locating target objects, where each particle is represented by an image of a single small FOV that is controlled and captured by a switching galvano-mirror. The object detector is used to determine if the particle image contains target objects. Then, to reduce the total scanning cost for capturing all particles, we introduce a center partitioned scanning algorithm that significantly speeds up the scanning process by 10× or more. Moreover, we optimize the processing pipeline for low latency sensing by using a high-parallelism visual feedback scheme to improve efficiency. Finally, an efficient and fast active vision system is implemented, which is based on a 2-axis galvano-mirror and a high-speed camera without multi-camera fusion for active object searching. Abundant experimental results demonstrate the effectiveness of our proposed system in indoor environments and real-world scenarios. We also verify the performance in terms of scanning speed and detection accuracy.