MultiClass Object Classification in Video Surveillance Systems - Experimental Study

Mohamed Elhoseiny, Amr Bakry, Ahmed M. Elgammal

Abstract: There is a growing demand in automated public safety systems for detecting unauthorized vehicle parking, intrusions, unintended baggage, etc. Object detection and recognition significantly impact these applications. Object detection and recognition are challenging problems in this context, since the purpose of the surveillance videos is to capture a wide landscape of the scene, resulting in small, low-resolution and occluded images for objects. In this paper, we present an experimental study on geometric and appearance features for outdoor video surveillance systems. We also studied the classification performance under two dimensionality reduction techniques (i.e. PCA and Entropy-Based feature Selection). As a result, we built an experimental framework for an object classification system for surveillance videos with different configurations.