44 Natural visual scenes are cluttered. In such scenes, many objects in the periphery 45 can be crowded – blocked from identification – simply because of the dense array of 46 clutter. Outside the fovea, crowding constitutes the fundamental limitation on object 47 recognition, and is thought to arise from the limited resolution of the neural mechanisms 48 that select and bind visual features in...

This paper presents a novel method for people counting in crowded scenes that combines the information gathered by multiple cameras to mitigate the problem of occlusion that commonly affects the performance of counting methods using single cameras. The proposed method detects the corner points associated to the people present in the scene and computes their motion vector. During the training st...

Natural visual scenes are cluttered. In such scenes, many objects in the periphery can be crowded, blocked from identification, simply because of the dense array of clutter. Outside of the fovea, crowding constitutes the fundamental limitation on object recognition and is thought to arise from the limited resolution of the neural mechanisms that select and bind visual features into coherent obj...

We present an efficient method for detecting and localizing anomalies in videos showing crowded scenes. Research on fully convolutional neural networks (FCNs) has shown the potentials of this technology for object detection and localization, especially in images. We investigate how to involve temporal data, and how to transform a supervised FCN into an unsupervised one such that the resulting F...

We describe a method that can detect specific human behaviors even in crowded surveillance video scenes. Our developed system recognizes specific behaviors based on the trajectories created by detecting and tracking people in a video. It detects people using an HOG descriptor and SVM classifier, and it tracks the regions by calculating the two-dimensional color histograms. Our system identifies...

This paper systematically investigates the effectiveness of different visual feature coding schemes for facilitating the learning of time-delayed dependencies among disjoint multi-camera views. Accurate inter-camera dependency estimation across nonoverlapping camera views is non-trivial especially in crowded scenes where inter-object occlusion can be severe and frequent, and when the degree of ...

State-of-the-art methods of people counting in crowded scenes rely on deep networks to estimate people density in the image plane. Perspective distortion effects are handled implicitly by either learning scale-invariant features or estimating density in patches of different sizes, neither of which accounts for the fact that scale changes must be consistent over the whole scene. In this paper, w...

We present CoMet, a novel approach for computing group’s cohesion and using that to improve robot’s navigation in crowded scenes. Our uses cohesion-metric builds on prior work social psychology. compute this metric by utilizing various visual features of pedestrians from an RGB-D camera on-board robot. Specifically, we detect characteristics corresponding the proximity between people, their rel...