Edge computing-based real-time passenger counting using a compact convolutional neural network

Abstract

Crowd counting from low-resolution images is a challenging task, in particular in the edge computing system. An embedded equipment is commonly incompetent at patch-based crowd counting with real-time performance. This work develops a real-time method to count passengers in a bus by using Nvidia TX2. The videos of entry are recorded by a camera up ahead, and the data suffer from severe occlusion, which makes designing handcrafted features difficult. The counting is performed by summing up pixel values of the density map estimated using a compact convolutional neural network (CCNN), which is robust to scale variations by employing skip connections. A weighted Euclidean loss is proposed to handle cluttered backgrounds and blurry foregrounds. The loss increases the activations in dense regions, but can restrain the activations in background regions. The counting results are further improved by smoothing, which utilizes constraints between consecutive frames. Comparisons with existing counting approaches, including patch-based and whole image-based approaches, are made on two benchmarking datasets. The results indicate the accuracy of CCNN in counting dense crowds. Moreover, the evaluated bus datasets verify the feasibility of CCNN in counting passengers from low-resolution input images with real-time performance on TX2.