A Grid-based Representation for Human Action Recognition

Human action recognition (HAR) in videos is a fundamental research topic in computer vision. It consists mainly in understanding actions performed by humans based on a sequence of visual observations. In recent years, HAR have witnessed significant progress, especially with the emergence of deep learning models. However, most of existing approaches for action recognition rely on information that is not always relevant for this task, and are limited in the way they fuse the temporal information. In this paper, we propose a novel method for human action recognition that encodes efficiently the most discriminative appearance information of an action with explicit attention on representative pose features, into a new compact grid representation. Our GRAR (Grid-based Representation for Action Recognition) method is tested on several benchmark datasets demonstrating that our model can accurately recognize human actions, despite intra-class appearance variations and occlusion challenges.Comment: Accepted on 25th International Conference on Pattern Recognition (ICPR 2020

ActAR: Actor-Driven Pose Embeddings for Video Action Recognition

Human action recognition (HAR) in videos is one of the core tasks of video understanding. Based on video sequences, the goal is to recognize actions performed by humans. While HAR has received much attention in the visible spectrum, action recognition in infrared videos is little studied. Accurate recognition of human actions in the infrared domain is a highly challenging task because of the redundant and indistinguishable texture features present in the sequence. Furthermore, in some cases, challenges arise from the irrelevant information induced by the presence of multiple active persons not contributing to the actual action of interest. Therefore, most existing methods consider a standard paradigm that does not take into account these challenges, which is in some part due to the ambiguous definition of the recognition task in some cases. In this paper, we propose a new method that simultaneously learns to recognize efficiently human actions in the infrared spectrum, while automatically identifying the key-actors performing the action without using any prior knowledge or explicit annotations. Our method is composed of three stages. In the first stage, optical flow-based key-actor identification is performed. Then for each key-actor, we estimate key-poses that will guide the frame selection process. A scale-invariant encoding process along with embedded pose filtering are performed in order to enhance the quality of action representations. Experimental results on InfAR dataset show that our proposed model achieves promising recognition performance and learns useful action representations

Clinical and genetic data of Huntington disease in Moroccan patients

Background: Huntington's disease (HD) occurs worldwide with prevalence varying from 0.1 to 10 /100,000 depending of the ethnic origin. Since no data is available in the Maghreb population, the aim of this study is to describe clinical and genetic characteristics of Huntington patients of Moroccan origin.Methods: Clinical and genetics data of 21 consecutive patients recruited from 2009 to 2014 from the outpatient clinic of six medical centers were analyzed. Statistical analysis was performed using descriptive statistics.Results: Twenty one patients from 17 families were diagnosed positive for the IT15 gene CAG expansion. Clinical symptoms were predominantly motor (19/21). Twelve patients had psychiatric and behavioral disorders, and 11 patients had cognitive disorders essentially of memory impairment. Analysis of genetic results showed that 5 patients had reduced penetrant (RP) alleles and 16 had fully penetrant (FP) alleles. The mean CAG repeat length in patients with RP alleles was 38.4 ± 0.54, and 45.37 ± 8.30 in FP alleles. The age of onset and the size of the CAG repeat length showed significant inverse correlation (p <0.001, r = -0.754).Conclusion: Clinical and genetic data of Moroccan patients are similar to those of Caucasian populations previously reported in the literature.Keywords: Huntington disease/diagnosis, Huntington disease/epidemiology, Huntington disease/genetics, Trinucleotide repeat expansio

Clinical and genetic data of Huntington disease in Moroccan patients

Background: Huntington's disease (HD) occurs worldwide with prevalence varying from 0.1 to 10 /100,000 depending of the ethnic origin. Since no data is available in the Maghreb population, the aim of this study is to describe clinical and genetic characteristics of Huntington patients of Moroccan origin. Methods: Clinical and genetics data of 21 consecutive patients recruited from 2009 to 2014 from the outpatient clinic of six medical centers were analyzed. Statistical analysis was performed using descriptive statistics. Results: Twenty one patients from 17 families were diagnosed positive for the IT15 gene CAG expansion. Clinical symptoms were predominantly motor (19/21). Twelve patients had psychiatric and behavioral disorders, and 11 patients had cognitive disorders essentially of memory impairment. Analysis of genetic results showed that 5 patients had reduced penetrant (RP) alleles and 16 had fully penetrant (FP) alleles. The mean CAG repeat length in patients with RP alleles was 38.4 \ub1 0.54, and 45.37 \ub1 8.30 in FP alleles. The age of onset and the size of the CAG repeat length showed significant inverse correlation (p <0.001, r = -0.754). Conclusion: Clinical and genetic data of Moroccan patients are similar to those of Caucasian populations previously reported in the literature