Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector

Multiple object tracking (MOT) in urban traffic aims to produce the trajectories of the different road users that move across the field of view with different directions and speeds and that can have varying appearances and sizes. Occlusions and interactions among the different objects are expected and common due to the nature of urban road traffic. In this work, a tracking framework employing classification label information from a deep learning detection approach is used for associating the different objects, in addition to object position and appearances. We want to investigate the performance of a modern multiclass object detector for the MOT task in traffic scenes. Results show that the object labels improve tracking performance, but that the output of object detectors are not always reliable.Comment: 13th International Symposium on Visual Computing (ISVC

Dataset of embodied perspective enhances self and friend-biases in perceptual matching

Acknowledgements This work was supported by the National Natural Science Foundation of China (Project 31371017) and Tsinghua University Initiative Scientific Research Programme (Project 20131089329).Peer reviewedPublisher PD

Arousal and Executive Alterations in Attention Deficit Hyperactivity Disorder (ADHD)

Attention deficit hyperactivity disorder (ADHD) is the most common neurobehavioral disorder in childhood and can significantly affect a child’s personal and social development and academic achievement. Taking into account the model of attentional networks proposed by Posner et al., the aim of the present study was to review the literature regarding two main explicative models of ADHD, i.e., the inhibition model and the cognitive-energetic model, by discussing behavioral and neurological evidence of both models and the limitations of each model. The review highlights evidence that favors the energetic model and points to an unstable arousal as a potential pathogenetic factor in ADHD