Fast Tracking via Spatio-Temporal Context Learning
This addresses the problem of real-time visual tracking for applications like surveillance or robotics, though it appears incremental as it builds on existing context-based methods.
The paper tackles visual tracking by exploiting spatio-temporal context with a Bayesian framework, achieving 350 frames per second and favorable performance against state-of-the-art methods in efficiency, accuracy, and robustness.
In this paper, we present a simple yet fast and robust algorithm which exploits the spatio-temporal context for visual tracking. Our approach formulates the spatio-temporal relationships between the object of interest and its local context based on a Bayesian framework, which models the statistical correlation between the low-level features (i.e., image intensity and position) from the target and its surrounding regions. The tracking problem is posed by computing a confidence map, and obtaining the best target location by maximizing an object location likelihood function. The Fast Fourier Transform is adopted for fast learning and detection in this work. Implemented in MATLAB without code optimization, the proposed tracker runs at 350 frames per second on an i7 machine. Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods in terms of efficiency, accuracy and robustness.