Augmented reality (AR) has proven to be an invaluable interactive medium to reduce
cognitive load by bridging the gap between the task-at-hand and relevant information by …

Abstract The problem of Multiple Object Tracking (MOT) consists in following the trajectory of
different objects in a sequence, usually a video. In recent years, with the rise of Deep …

In today's scenario, there are frequent events (viz. political rallies, live concerts, strikes,
sports meet) occur in which many people gather to participate in the event. In crowded areas …

Object tracking belongs to active research areas in computer vision. We are interested in
matching-based trackers exploiting deep machine learning known as Siamese trackers …

Multi-object tracking has seen a lot of progress recently, albeit with substantial annotation
costs for developing better and larger labeled datasets. In this work, we remove the need for …

This paper proposes a novel method for online Multi-Object Tracking (MOT) using Graph
Convolutional Neural Network (GCNN) based feature extraction and end-to-end feature …

The supervised deep networks have shown great potential in improving the classification
performance. However, training these supervised deep networks is very challenging for …

Highlights•First use of number of housed animals to universally improve tracking
performance.•Developed an integrated deep learning network for on-line tracking.•Full use …

This paper explores deep learning (DL) methods that are used or have the potential to be
used for traffic video analysis, emphasising driving safety for both autonomous vehicles and …

视觉多目标跟踪是计算机视觉领域的热点问题, 然而, 场景中目标数量的不确定,
目标之间的相互遮挡, 目标特征区分度不高等多种难题导致了视觉多目标跟踪现实应用进展缓慢 …