Evaluation of Urban Vehicle Tracking Algorithms

Sep 16, 2015

Download: MP4 Video Size: 105.4MB  
Watch on YouTube

Abstract

Low signal-to-noise data processing algorithms for
improved detection, tracking, discrimination and situational
threat assessment are a key research challenge. As sensor
technologies progress, the number of pixels will increase significantly.
This will result in increased resolution, which could
improve object discrimination, but unfortunately, will also result
in a significant increase in the number of potential targets to
track. Many tracking techniques, like multi-hypothesis trackers,
suffer from a combinatorial explosion as the number of
potential targets increase. As the resolution increases, the phenomenology
applied towards detection algorithms also changes.
For low resolution sensors, blob tracking is the norm. For
higher resolution data, additional information may be employed
in the detection and classification steps. The most challenging
scenarios are those where the targets cannot be fully resolved,
yet must be tracked and distinguished for neighboring closely
spaced objects. Tracking vehicles in an urban environment is an
example of such a challenging scenario. This report evaluates
several potential tracking algorithms for large-scale tracking in
an urban environment. The algorithms considered are: random
sample consensus (RANSAC), Markov chain Monte Carlo data
association (MCMCDA), tracklet inference from factor graphs,
and a proximity tracker. Each algorithm was tested on a
combination of real and simulated data and evaluated against
a common set of metrics.