Dual-layer particle filtering for simultaneous multiple objects detection and tracking

Abstract

In this paper, we present a novel method for simultaneous detection and tracking of multiple objects using dual-layer particle filtering. For detecting and tracking multiple moving objects, the proposed dual-layer particle filter (DLPF) consists of parent-particles (PPs) in the first layer for detecting multiple objects and child-particles (CPs) in the second layer for tracking objects that are detected in the first layer. In the first layer, PPs detect persons using a classifier prior trained by the intersection kernel support vector machine (IKSVM) at each particle under a randomly selected scale. If a certain PP detects a person, it generates CPs, and makes an object model in the detected object region for tracking the detected object. While PPs that have detected objects generate CPs for tracking, the rest of PPs still move for detecting objects. Experimental results show that the proposed method can automatically detect and track multiple objects and efficiently reduce the computational time using the sampled particles based on motion distribution in video sequences.