In this work, we present a Multi-Channel deep convolutional Pyramid Person Matching Network (MC-PPMN) based on the combination of the semantic-components and the colortexture distributions to address the problem of person reidentification. In particular, we learn separate deep representations for semantic-components and color-texture distributions from two person images and then employ pyramid ...

We address the problem of detecting human violence in image sequences, such as fist fighting, kicking, hitting with objects, etc. To detect violence we rely on motion trajectory information and on orientation information of a person’s limbs. We define an Acceleration Measure Vector (AMV) composed of direction and magnitude of motion and we define jerk to be the temporal derivative of AMV. We pr...

In this paper, we investigate different usages of feature representations in the web person name disambiguation task which has been suffering from the mismatch of vocabulary and lack of clues in web environments. In literature, the latter receives less attention and remains more challenging. We explore the feature space in this task and argue that collecting person specific evidences from a cor...

In this paper, we extend the notion of person by person optimization to binary decision spaces. The novelty of our approach is the adaptation to a dynamic team context of notions borrowed from the pseudo-boolean optimization field as completely local-global or unimodal functions and sub-modularity. We also generalize the concept of pbp optimization to the case where the Decision Makers (DMs) ma...

Three studies investigated conditions in which perceivers view dispositions and situations as interactive, rather than independent, causal forces when making judgments about another’s personality. Study 1 showed that perceivers associated 5 common trait terms (e.g., friendly and shy) with characteristic if . . . then . . . (if situation a, then the person does x, but if situation b, then the pe...

Despite of the recent success of neural networks for human pose estimation, current approaches are limited to pose estimation of a single person and cannot handle humans in groups or crowds. In this work, we propose a method that estimates the poses of multiple persons in an image in which a person can be occluded by another person or might be truncated. To this end, we consider multiperson pos...