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The visual recognition and understanding of human actions remain an active research domain of computer vision, being the scope of various research works over the last two decades. The problem is challenging due to its many interpersonal variations in appearance and motion dynamics between humans, without forgetting the environmental heterogeneity between different video images. This complexity splits the problem into two major categories: action classification, recognising the action being performed in the scene, and spatiotemporal action localisation, concerning recognising multiple localised human actions present in the scene. Previous surveys mainly focus on the evolution of this field, from handcrafted features to deep learning architectures. However, this survey presents an overview of both categories and respective evolution within each one, the guidelines that should be followed and the current benchmarks employed for performance comparison between the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

It is known that the kinematics of the human body skeleton reveals valuable information in action recognition. Recently, modeling skeletons as spatio-temporal graphs with Graph Convolutional Networks (GCNs) has been reported to solidly advance the state-of-the-art performance. However, GCN-based approaches exclusively learn from raw skeleton data, and are expected to extract the inherent structural information on their own. This paper describes REGINA, introducing a novel way to REasoning Graph convolutional networks IN Human Action recognition. The rationale is to provide to the GCNs additional knowledge about the skeleton data, obtained by handcrafted features, in order to facilitate the learning process, while guaranteeing that it remains fully trainable in an end-to-end manner. The challenge is to capture complementary information over the dynamics between consecutive frames, which is the key information extracted by state-of-the-art GCN techniques. Moreover, the proposed strategy can be easily integrated in the existing GCN-based methods, which we also regard positively. Our experiments were carried out in well-known action recognition datasets and enabled to conclude that REGINA contributes for solid improvements in performance when incorporated to other GCN-based approaches, without any other adjustment regarding the original method. For reproducibility, the REGINA code and all the experiments carried out will be publicly available at https://github.com/DegardinBruno. [ABSTRACT FROM AUTHOR]

• We introduce a self-supervised learning framework for abnormal events detection. • We describe a novel term added to the state-of-the-art loss function that spreads the scores in the unit interval. • We report a reproducible empirical evaluation between the state-of-the-art techniques in abnormal events detection. • We propose an ensemble of three experts that consistently advances the abnormal events detection state-of-the-art. • We announce the free availability of a fully annotated dataset for detection of 'Fights' in surveillance footage. [Display omitted] The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works. Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with four-fold contributions: 1) upon the work of Sultani et al., we introduce one iterative learning framework composed of two experts working in the weak and self-supervised paradigms and providing additional amounts of learning data to each other, where the novel instances at each iteration are filtered by a Bayesian framework that supports the iterative data augmentation task; 2) we describe a novel term that is added to the baseline loss to spread the scores in the unit interval, which is crucial for the performance of the iterative framework; 3) we propose a Random Forest ensemble that fuses at the score level the top performing methods and reduces the EER values about 20% over the state-of-the-art; and 4) we announce the availability of the "UBI-Fights" dataset, fully annotated at the frame level, that can be freely used by the research community. The code, details of the experimental protocols and the dataset are publicly available at http://github.com/DegardinBruno/. [ABSTRACT FROM AUTHOR]