Efficient object detection and classification on low power embedded systems

Identifying real world 3D objects such as pedestrians, vehicles and traffic signs using 2D images is a challenging task. There are multiple approaches to tackle this problem with varying degree of detection accuracy and implementation complexity. Some approaches use “hand coded” object features such as Histogram of Oriented Gradients (HOG), Haar, Scale Invariant Feature Transform (SIFT) along with a linear classifier such as Support Vector Machine (SVM), Adaptive Boosting (AdaBoost) to detect objects. Recent developments have shown that a deep multi-layered Convolution Neural Network (CNN) classifier can learn the object features on its own and also classify at an accuracy surpassing human vision. In this paper we combine both the approaches; “object detection” is done using HOG features and AdaBoost cascade classifier and “object classification” is done using CNN to classify the type of objects being detected. The proposed method is implemented on TI's low power TDA3x SoC.