Enhanced Object Detection with Deep Convoluti...

Enhanced Object Detection with Deep Convolutional Neural Networks

Author Name : Vartika Srivastava, Dr. Radhey Shyam

ABSTRACT

Object visual detection (OVD) aims to extract accurate real-time on-road environment information, which involves three phases detection of objects of interest, recognition of detected objects, and tracking of objects in motion. Here OpenCV tool provide the algorithm support for different object detection. Since recognition and tracking often rely on the results from detection, the ability to detect objects of interest effectively plays a crucial role in OVD. In this paper, we focus on three important classes of objects: traffic signs, cars, and cyclists. We propose to detect all the three important objects in a single learning-based detection framework (SLDF). The proposed framework consists of a dense feature extractor and detectors of three important classes. Once the dense features have been extracted, these features are shared with all detectors. The advantage of using one common framework is that the detection speed is much faster, since all dense features need only to be evaluated once in the testing phase. In contrast, most previous works have designed specific detectors using different features for each of these three classes. To enhance the feature robustness to noises and image deformations, we introduce spatially pooled features as a part of aggregated channel features. In order to further improve the generalization performance, we propose an object sub categorization method as a means of capturing the intra class variation of objects. We experimentally demonstrate the effectiveness and efficiency of the proposed framework in three detection applications: traffic sign detection, car detection, and cyclist detection. The proposed framework achieves the competitive performance with state-of-the-art approaches on several benchmark data sets.

Keywords: Object visual detection (OVD),single learning-based detection framework (SLDF),traffic signs, OpenCV, cyclists.