Pedestrian recognition is one of the basic elements of an active pedestrian protection system. Especially, there have been various researches to develop vision-based pedestrian classifiers. Recently, Munder and Gavrila (IEEE Trans Pattern Anal Mach Intell 28(11):1863-1868, 2006) opened their DaimlerChrysler (DCX) pedestrian image database and tried to provide an objective comparison between popular features and classifiers. After their publication, objective performance comparison of features, classifiers and architectures became possible. This paper reports four experimental results with the DCX database. First, the Gabor filter bank-based feature is competent in pedestrian recognition. Second, optimization of a classifier using performance estimator greatly enhances the performance of the resultant classifier. Third, once the imaging system uses histogram equalization and the same capturing method as a database, a practical pedestrian recognition system can be constructed with a public open database, without the necessity of re-training using the new database acquired with the actual imaging system. Fourth, a posteriori probability-based post-processing increases the recognition rate of consecutive image sequencing while maintaining a false positive rate.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Artificial Intelligence