Abstract

Background: Real-time object detection has been attracting much attention recently due the increasing market need of such systems. Therefore, different detection algorithms and techniques have been evaluated to create a reliable detection system. The main challenge to implement a realtime reliable detection system relies on the algorithm training phase. During this phase, a large number of object image database needs to be prepared for each object to be detected.

Objective: In this work, we implement a simultaneous object detection system based on local Edge Orientation Histograms (EOH) as feature extraction method with a smaller objects image database. Then, we evaluate the performance of this detection system in two separate platforms.

Methods: We evaluated the performance of the detection of Ede Orientation Histograms against HAAR and Local Binary Patterns (LBP) algorithms using two different objects. After that, we discussed the evaluation of the detection systems on the standard platform in addition to the porting process into the embedded platform.

Results: We achieved excellent results on both face and hands objects using less than 300 samples. This number is really negligible compared to the size of the image database used by state-of-the-art solutions. In terms of quality of detection, we have achieved more than 93% detection accuracy for the standard platform and 91.8% in the embedded platform for both face and hand objects.

Conclusion: In this work, we demonstrated how Edge Orientation Histograms-based detection system gives better performance results than the algorithms evaluated against with less than 300 images database in two separate platforms.

Keywords: Computer vision, object detection, face detection, detection performance, real-time object detection, EOH, LBP, Haar-like.

Graphical Abstract