Open public places, such as pedestrian streets, parks, and squares, are vulnerable when the pedestrians thronged into sidewalks. The crowd count changes dynamically over time with various external factors, surroundings, weekends, peak hours, so it is essential to predict accurate timely count. To address this issue, study introduces graph convolutional network (GCN), a network-based model, flow...

Compared to other applications in computer vision, convolutional neural networks have under-performed on pedestrian detection. A breakthrough was made very recently by using sophisticated deep CNN models, with a number of hand-crafted features [1], or explicit occlusion handling mechanism [2]. In this work, we show that by re-using the convolutional feature maps (CFMs) of a deep convolutional n...

Road scene segmentation is important in computer vision for different applications such as autonomous driving and pedestrian detection. Recovering the 3D structure of road scenes provides relevant contextual information to improve their understanding. In this paper, we use a convolutional neural network based algorithm to learn features from noisy labels to recover the 3D scene layout of a road...

Pedestrian Detection is the most critical module of an Autonomous Driving system. Although a camera commonly used for this purpose, its quality degrades severely in low-light night time driving scenarios. On other hand, thermal image remains unaffected similar conditions. This paper proposes end-to-end multimodal fusion model pedestrian detection using RGB and images. Its novel spatio-contextua...

Pedestrian detection is a significant research topic in the computer vision (CV) domain for longer period. Recently, deep learning (DL) and specifically convolutional neural network (CNN) exhibit improvement tasks such as object detection, segmentation, image classification, etc. With this motivation, study develops novel Colliding Bodies Optimization with Deep Learning based Robust Detection (...