Subsequence-Wise Approach for Online Tracking

1.1 Existing Approaches

Lots of algorithms have been proposed to deal with this task, from the simple feature point matching method to non-rigid object tracking. The general idea inside these approaches can be simply described as two steps: (1) make use of the information available to model the target object or both the target and background; (2) decide where the target is in the current frame. For example, particle filter based tracking algorithm (Arulampalam, 2002, Isard, 1998) adopts the information available in the past frames to get the priori probability of the target’s state for the current frame, and then the measurements are used to get the posterior probability distribution function via Bayesian Theorem. Based on the posterior the state in the current frame of the target is estimated. Mean shift based tracking algorithm (Cheng, 1995, Comaniciu, 2000) treats tracking as a mode seeking process. The model of the target for tracking is constructed based on the passed frames, and then it deploys the mean shift method to search the optimal mode the current frame. These algorithms make a decision based on the feature extracted from the current frame, while they fail to take into account the constraint among the decisions in consecutive frames, which we call a subsequence. Some other algorithms (Grabner, 2006, Nguyen, 2006) are aimed to explore not only the spatial context of the object but also the temporal spatial context. However, they still make one decision at a time, which is for the current frame, and do not consider the innate relation among the decisions in neighboring frames.

Now, to express our idea clearly, we introduce the term tracking unit. Here, we call a repeated component of a video sequence a tracking unit, if and only if the tracking algorithm treats each such component equivalently. For example, these algorithms listed above treat every frame equivalently, and make a decision in an individual frame, no matter whether they make use of the temporal context. Therefore, the tracking unit for them is a single frame. While experiences tell us that when we humans track a target using our eyes, we do not make decisions about the states in several continuous frames separately. Or rather, we will explore some inner relation among these decisions and fuse them before releasing the decision result. For instance, we are able to estimate the occluded target states if we know how the target enters occlusion and how it gets out. Therefore, it is intuitive that the constraint among decisions in consecutive frames will be helpful for our final better decisions if we can make use of it. From this example, we can further see that choosing a frame as the tracking unit may be not a good choice, for it probably fails to estimate the target’s state when it is occluded. Before seeking a better choice for tracking unit, we denote these algorithms mentioned above as frame-wise approach, since they take a single frame as the tracking unit.