Joint Region Tracking with Switching Hypothesized Measurements

This paper proposes a switching hypothesized measurements (SHM) model supporting multimodal probability distributions and presents the application of the model in handling potential variability in visual environments when tracking multiple objects jointly. For a set of occlusion hypotheses, a frame is measured once under each hypothesis, resulting in a set of measurements at each time instant. A computationally efficient SHM filter is derived for online joint region tracking. Both occlusion relationships and states of the objects are recursively estimated from the history of hypothesized measurements. The reference image is updated adaptively to deal with appearance changes of the objects. The SHM model is generally applicable to various dynamic processes with multiple alternative measurement methods.

• Type: Conference
• Year: 2003
• Where: ICCV
• Authors: Kia-Fock Loe, Tele Tan, Yang Wang 0002
• Keywords: Computer Vision, Efficient Shm Filter, ICCV 2003, Multimodal Probability Distributions, Occlusion Relationships, SHM Model, Switching Hypothesized Measurements