Lightweight network and parallel computing for fast pedestrian detection

Abstract

Summary In recent years, researchers have made great efforts in computer vision task (e.g., object detection) with the widely use of convolutional neural networks (CNNs). However, object detection algorithms based on CNNs suffer from high computation cost even on the high‐performance computers. In addition, with the development of high‐resolution videos, the deployment of object detection algorithms becomes more and more difficult because of the large amount of data, let alone the portable platforms, such as unmanned aerial vehicles (UAVs). In this paper, we research a lightweight network on portable platform for outdoor tiny pedestrian detection. Concretely, we first set up a training dataset manually for lack of tiny pedestrian samples in common datasets. We provide a lightweight network, and then, parallel computing is introduced to make the most of the advantage of GPU. Finally, our method can achieve real‐time performance on Jetson TX2. Experimental results verify that the proposed model has promising performance in tiny pedestrian detection designed for portable GPU platforms.

A novel FCNs‐ConvLSTM network for video salient object detection

“…Traditional image saliency detection methods are primarily to detect contrast between salient regions and surroundings by handcrafted features. More recently, with the wide application of machine learning in computer vision and many other fields, 5–7 some technologies based on deep learning, including convolutional neural networks (CNNs) in a supervised learning mode and spatiotemporal cascade neural network (SCNN) in a weakly supervised learning mode are proposed for saliency detection.…”