Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

A robust and efficient video representation for action recognition

This paper introduces a state-of-the-art video representation and applies it to efficient action recognition and detection. We first propose to improve the popular dense trajectory features by explicit camera motion estimation. More specifically, we extract feature point matches between frames using SURF descriptors and dense optical flow. The matches are used to estimate a homography with RANSAC. To improve the robustness of homography estimation, a human detector is employed to remove outlier matches from the human body as human motion is not constrained by the camera. Trajectories consistent with the homography are considered as due to camera motion, and thus removed. We also use the homography to cancel out camera motion from the optical flow. This results in significant improvement on motion-based HOF and MBH descriptors. We further explore the recent Fisher vector as an alternative feature encoding approach to the standard bag-of-words histogram, and consider different ways to include spatial layout information in these encodings. We present a large and varied set of evaluations, considering (i) classification of short basic actions on six datasets, (ii) localization of such actions in feature-length movies, and (iii) large-scale recognition of complex events. We find that our improved trajectory features significantly outperform previous dense trajectories, and that Fisher vectors are superior to bag-of-words encodings for video recognition tasks. In all three tasks, we show substantial improvements over the state-of-the-art results

The influence of visual landscape on the free flight behavior of the fruit fly Drosophila melanogaster

To study the visual cues that control steering behavior in the fruit fly Drosophila melanogaster, we reconstructed three-dimensional trajectories from images taken by stereo infrared video cameras during free flight within structured visual landscapes. Flies move through their environment using a series of straight flight segments separated by rapid turns, termed saccades, during which the fly alters course by approximately 90° in less than 100 ms. Altering the amount of background visual contrast caused significant changes in the fly’s translational velocity and saccade frequency. Between saccades, asymmetries in the estimates of optic flow induce gradual turns away from the side experiencing a greater motion stimulus, a behavior opposite to that predicted by a flight control model based upon optomotor equilibrium. To determine which features of visual motion trigger saccades, we reconstructed the visual environment from the fly’s perspective for each position in the flight trajectory. From these reconstructions, we modeled the fly’s estimation of optic flow on the basis of a two-dimensional array of Hassenstein–Reichardt elementary motion detectors and, through spatial summation, the large-field motion stimuli experienced by the fly during the course of its flight. Event-triggered averages of the large-field motion preceding each saccade suggest that image expansion is the signal that triggers each saccade. The asymmetry in output of the local motion detector array prior to each saccade influences the direction (left versus right) but not the magnitude of the rapid turn. Once initiated, visual feedback does not appear to influence saccade kinematics further. The total expansion experienced before a saccade was similar for flight within both uniform and visually textured backgrounds. In summary, our data suggest that complex behavioral patterns seen during free flight emerge from interactions between the flight control system and the visual environment

Detecção de eventos complexos em vídeos baseada em ritmos visuais

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O reconhecimento de eventos complexos em vídeos possui várias aplicações práticas relevantes, alavancadas pela grande disponibilidade de câmeras digitais instaladas em aeroportos, estações de ônibus e trens, centros de compras, estádios, hospitais, escolas, prédios, estradas, entre vários outros locais. Avanços na tecnologia digital têm aumentado as capacidades dos sistemas em reconhecer eventos em vídeos por meio do desenvolvimento de dispositivos com alta resolução, dimensões físicas pequenas e altas taxas de amostragem. Muitos trabalhos disponíveis na literatura têm explorado o tema a partir de diferentes pontos de vista. Este trabalho apresenta e avalia uma metodologia para extrair características dos ritmos visuais no contexto de detecção de eventos em vídeos. Um ritmo visual pode ser visto com a projeção de um vídeo em uma imagem, tal que a tarefa de análise de vídeos é reduzida a um problema de análise de imagens, beneficiando-se de seu baixo custo de processamento em termos de tempo e complexidade. Para demonstrar o potencial do ritmo visual na análise de vídeos complexos, três problemas da área de visão computacional são selecionados: detecção de eventos anômalos, classificação de ações humanas e reconhecimento de gestos. No primeiro problema, um modelo e? aprendido com situações de normalidade a partir dos rastros deixados pelas pessoas ao andar, enquanto padro?es representativos das ações são extraídos nos outros dois problemas. Nossa hipo?tese e? de que vídeos similares produzem padro?es semelhantes, tal que o problema de classificação de ações pode ser reduzido a uma tarefa de classificação de imagens. Experimentos realizados em bases públicas de dados demonstram que o método proposto produz resultados promissores com baixo custo de processamento, tornando-o possível aplicar em tempo real. Embora os padro?es dos ritmos visuais sejam extrai?dos como histograma de gradientes, algumas tentativas para adicionar características do fluxo o?tico são discutidas, além de estratégias para obter ritmos visuais alternativosAbstract: The recognition of complex events in videos has currently several important applications, particularly due to the wide availability of digital cameras in environments such as airports, train and bus stations, shopping centers, stadiums, hospitals, schools, buildings, roads, among others. Moreover, advances in digital technology have enhanced the capabilities for detection of video events through the development of devices with high resolution, small physical size, and high sampling rates. Many works available in the literature have explored the subject from different perspectives. This work presents and evaluates a methodology for extracting a feature descriptor from visual rhythms of video sequences in order to address the video event detection problem. A visual rhythm can be seen as the projection of a video onto an image, such that the video analysis task can be reduced into an image analysis problem, benefiting from its low processing cost in terms of time and complexity. To demonstrate the potential of the visual rhythm in the analysis of complex videos, three computer vision problems are selected in this work: abnormal event detection, human action classification, and gesture recognition. The former problem learns a normalcy model from the traces that people leave when they walk, whereas the other two problems extract representative patterns from actions. Our hypothesis is that similar videos produce similar patterns, therefore, the action classification problem is reduced into an image classification task. Experiments conducted on well-known public datasets demonstrate that the method produces promising results at high processing rates, making it possible to work in real time. Even though the visual rhythm features are mainly extracted as histogram of gradients, some attempts for adding optical flow features are discussed, as well as strategies for obtaining alternative visual rhythmsMestradoCiência da ComputaçãoMestre em Ciência da Computação1570507, 1406910, 1374943CAPE