Kalman Filters in Constrained Model Based Tracking

Abstract

Model-based vision allows the recovery and tracking of the 3D position and orientation of a known object from a sequence of images. A Kalman filter can be used to improve the tracking stability with three main benefits. Firstly it is an optimal filter in the least squares sense, with the added advantage that the physical dynamics and constraints of the tracking problem can easily be built into the system model. Secondly the measurement model allows for uncertainty in the measurement of the recovered model position. Thirdly, data on the empirical error are generated which can, for example, be used to control the model matching process used in the tracking.

L1 tracker with spatially weighted similarity measure based clustering

“…In the literature, it is usually casted as sequential state estimation of moving targets for each frame from a video sequence. There are two typical frameworks popular in visual tracking: Kalman filter [6], [7], [8], [9] and Particle filter [10], [11], [12], [13]. The former has optimal solution which only restricted to linear Gaussian dynamic system, in contrast, the latter is rather more flexible in nonlinear/non-Gaussian cases and has thus been more widely used.…”