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Abstract Recently, sparse representation has been applied to visual tracking to find the target with the minimum reconstruction error from a target template subspace. Though effective, these L1 trackers require high computational costs due to numerous calculations for l1 minimization. In addition, the inherent occlusion insensitivity of the l1 minimization has not been fully characterized. In this system, we propose an efficient L1 tracker, named bounded particle resampling (BPR)-L1 tracker, with a minimum error bound and occlusion detection. First, the minimum error bound is calculated from a linear least squares equation and serves as a guide for particle resampling in a particle filter (PF) framework. Most of the insignificant samples are removed before solving the computationally expensive l1 minimization in a two-step testing.
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Efficient Minimum Error Bounded Particle Resampling L1
Tracker With Occlusion Detection
Abstract
Recently, sparse representation has been applied to visual tracking to find the
target with the minimum reconstruction error from a target template subspace. Though
effective, these L1 trackers require high computational costs due to numerous
calculations for l1 minimization. In addition, the inherent occlusion insensitivity of the
l1 minimization has not been fully characterized. In this system, we propose an efficient
L1 tracker, named bounded particle resampling (BPR)-L1 tracker, with a minimum
error bound and occlusion detection. First, the minimum error bound is calculated from
a linear least squares equation and serves as a guide for particle resampling in a particle
filter (PF) framework. Most of the insignificant samples are removed before solving the
computationally expensive l1 minimization in a two-step testing. The first step, named τ
testing, compares the sample observation likelihood to an ordered set of thresholds to
remove insignificant samples without loss of resampling precision. The second step,
named max testing, identifies the largest sample probability relative to the target to
further remove insignificant samples without altering the tracking result of the current
frame. Though sacrificing minimal precision during resampling, max testing achieves
significant speed up on top of τ testing. The BPR-L1 technique can also be beneficial to
other trackers that have minimum error bounds in a PF framework, especially for
trackers based on sparse representations. After the error-bound calculation, BPR-L1
performs occlusion detection by investigating the trivial coefficients in the l1
minimization. These coefficients, by design, contain rich information about image
corruptions, including occlusion. Detected occlusions are then used to enhance the
template updating. For evaluation, we conduct experiments on three video applications:
biometrics (head movement, hand holding object, singers on stage), pedestrians (urban
travel, hallway monitoring), and cars in traffic (wide area motion imagery, ground-
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mounted perspectives). The proposed BPR-L1 method demonstrates an excellent
performance as compared with nine state-of-the-art trackers on eleven challenging
benchmark sequences.
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