Object classification in traffic scenes using multiple spatio-temporal features

Guruprasad Somasundaram, Vassilios Morellas, Nikolaos Papanikolopoulos, Saad Bedros

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Object classification is a widely researched area in the field of computer vision. Lately there has been a lot of attention to appearance based models for representing objects. The most important feature of classifying objects such as pedestrians, vehicles, etc. in traffic scenes is that we have motion information available to us. The motion information presents itself in the form of temporal cues such as velocity and also as spatio-temporal cues such as optical flow, DHOG [6], etc. We propose a novel spatio-temporal feature based on covariance descriptors known as DCOV which captures complementary information to the DHOG feature. We present a combined classifier based on properties of tracked objects along with the DHOG and the DCOV features. We show based on experiments on the PETS 2001 dataset and two video sequences of bicycle and pedestrian traffic that the proposed classifier provides competent performance for distinguishing pedestrians, vehicles and bicyclists. Our method is also adaptive and benefits from the availability of more data for training. The classifier is also developed with real-time feasibility in mind.

Original languageEnglish (US)
Title of host publication2012 20th Mediterranean Conference on Control and Automation, MED 2012 - Conference Proceedings
Pages1536-1541
Number of pages6
DOIs
StatePublished - Oct 5 2012
Event2012 20th Mediterranean Conference on Control and Automation, MED 2012 - Barcelona, Spain
Duration: Jul 3 2012Jul 6 2012

Publication series

Name2012 20th Mediterranean Conference on Control and Automation, MED 2012 - Conference Proceedings

Other

Other2012 20th Mediterranean Conference on Control and Automation, MED 2012
Country/TerritorySpain
CityBarcelona
Period7/3/127/6/12

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