Publication
AVSS 2014
Conference paper

Long-term object tracking for parked vehicle detection

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Abstract

We develop a robust approach to detect parked vehicles in real time. Our approach particularly focuses on tracking vehicles in long term under challenging conditions such as lighting changes and occlusions. Vehicle tracking is performed by template matching based on fast-computed corner points. The template model is made self-adaptive over time to accommodate lighting changes. We also present an effective way to manage and track multiple vehicles when they are parked close together and occlude one another. We demonstrate the effectiveness of our approach on the challenging i-LIDs data set and another large one collected from real-world scenarios.

Date

Publication

AVSS 2014

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