University of Houston

Multiple pedestrian tracking in unconstrained environments is an important task that has received considerable attention from the computer vision community in the past two decades. A number of approaches that address this issue have been proposed. Accurate multiple pedestrian tracking can greatly improve the performance of activity recognition and analysis of high level events through a surveillance system. However, the complexity of human motion poses several challenges to the accuracy and precision of a tracking system. Traditional approaches to pedestrian tracking assume that each pedestrian walks independently and the tracker predicts the location based on an underlying motion model, such as a constant velocity or an autoregressive model.

In this work, we propose to investigate the mechanisms of human interactions that can be integrated to realize a novel visual tracking method. In our initial efforts, we have considered complex social interactions and designed an algorithm that aims to estimate human intentions that could motivate their motion. We integrate these multiple social interaction modes or human intentions into an interactive Markov Chain Monte Carlo tracker. We demonstrate how the developed method translates into a more informed motion prediction, resulting in a robust tracking performance. We test our method on videos from unconstrained outdoor environments and compare it against popular multi-object trackers.

To better inform and guide the tracking framework, we have also developed a novel approach for multi-target tracking using an ensemble framework that optimally chooses target tracking results from that of independent trackers and a detector at each time step. The compound model is designed to select the best candidate scored by a function integrating detection confidence, appearance affinity, and smoothness constraints imposed using geometry and motion information. Parameters of our association score function are discriminatively trained with a max-margin framework. Optimal selection is achieved through a hierarchical data association step that progressively associates candidates to targets. By introducing a second target classifier and using the ranking score from the pre-trained classifier as the detection confidence measure, we add additional robustness against unreliable detections. The proposed algorithm robustly tracks a large number of moving objects in complex scenes with occlusions. We evaluate our approach on a variety of public datasets and show promising improvements over state-of-the-art methods.