Vehicle Detection and Classification in Aerial Imagery
Yi Tan, Yanjun Xu, Subhodev Das, Ali Khalid Chaudhry · 2018
We present a novel method for vehicle detection and classification in aerial imagery. First, change detection analyzes a pair of mutually aligned images captured at the same location but at different time instants to generate vehicle proposals. Next, a trained Convolutional Neural Network (CNN) classifier is applied to (1) determine if a proposal truly contains a vehicle, and (2) classify any vehicle present into major categories. Experimental results using infrared (IR) data demonstrate the efficacy of our method: vehicle detection rate is over 99%; light-duty vehicles (e.g. sedan) are classified with 89% accuracy, medium-duty vehicles (e.g., van and pickup) with 79% and heavy-duty vehicles (e.g., trucks and buses) with 73% accuracies. The classification performance difference among different vehicle types is attributed to the size of training samples available in each category. Our system can process data at video rate making it feasible for real-time traffic monitoring.