Object Trajectory

An Algorithm for Multiple Object Trajectory Tracking

Mei Han Wei Xu Hai Tao‡ Yihong Gong NEC Laboratories America, Cupertino, CA, USA {meihan, xw, ygong}@sv.nec-labs.com ‡ University of California at Santa Cruz, Santa Cruz, CA, USA tao@soe.ucsc.edu

Abstract

Most tracking algorithms are based on the maximum a posteriori (MAP) solution of a probabilistic framework called Hidden Markov Model, where the distribution of the object state at current time instance is estimated based on current and previous observations. However, this approach is prone to errors caused by temporal distractions such as occlusion, background clutter and multi-object confusion. In this paper we propose a multiple object tracking algorithm that seeks the optimal state sequence which maximizes the joint state-observation probability. We name this algorithm trajectory tracking since it estimates the state sequence or “trajectory” instead of the current state. The algorithm is capable of tracking multiple objects whose number is unknown and varies during tracking. We introduce an observation model which is composed of the original image, the foreground mask given by background subtraction and the object detection map generated by an object detector. The image provides the object appearance information. The foreground mask enables the likelihood computation to consider the multi-object configuration in its entirety. The detection map consists of pixelwise object detection scores, which drives the tracking algorithm to perform joint inference on both the number of objects and their configurations efficiently.

to the forward algorithm in HMM literature. However, this approach may fail with background clutter, occlusion and multi-object confusion. Another type of tracking algorithms estimates the joint state-observation sequence distribution. The tracking result corresponds to the state sequence which maximizes the joint probability between the state sequence and the observation sequence. The...