Tracking in low frame rate video: a cascade particle filter with discriminative observers of different life spans.
Tracking objects in low frame rate (LFR) video or with abrupt motion poses two main difficulties which most conventional tracking methods can hardly handle: 1) poor motion continuity and increased search space; 2) fast appearance variation of target and more background clutter due to increased search space. In this paper, we address the problem from a view which integrates conventional tracking and detection, and present a temporal probabilistic combination of discriminative observers of different lifespans. Each observer is learned from different ranges of samples, with different subsets of features, to achieve varying levels of discriminative power at varying cost. An efficient fusion and temporal inference is then done by a cascade particle filter which consists of multiple stages of importance sampling. Experiments show significantly improved accuracy of the proposed approach in comparison with existing tracking methods, under the condition of LFR data and abrupt motion of both target and camera.
Li
Yuan
Y
Department of Computer Science, University of Southern California, Los Angeles, CA 90089, USA. yli8@usc.edu
Ai
Haizhou
H
Yamashita
Takayoshi
T
Lao
Shihong
S
Kawade
Masato
M
D000465:Algorithms; D001185:Artificial Intelligence; D016002:Discriminant Analysis; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014741:Video Recording / Q000379:methods*