Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients

In this paper we propose a novel approach to multi-action recognition that performs joint segmentation and classification. This approach models each action using a Gaussian mixture using robust low-dimensional action features. Segmentation is achieved by performing classification on overlapping temporal windows, which are then merged to produce the final result. This approach is considerably less complicated than previous methods which use dynamic programming or computationally expensive hidden Markov models (HMMs). Initial experiments on a stitched version of the KTH dataset show that the proposed approach achieves an accuracy of 78.3%, outperforming a recent HMM-based approach which obtained 71.2%

TREAT: Terse Rapid Edge-Anchored Tracklets

Fast computation, efficient memory storage, and performance on par with standard state-of-the-art descriptors make binary descriptors a convenient tool for many computer vision applications. However their development is mostly tailored for static images. To respond to this limitation, we introduce TREAT (Terse Rapid Edge-Anchored Tracklets), a new binary detector and descriptor, based on tracklets. It harnesses moving edge maps to perform efficient feature detection, tracking, and description at low computational cost. Experimental results on 3 different public datasets demonstrate improved performance over other popular binary features. These experiments also provide a basis for benchmarking the performance of binary descriptors in video-based applications

BiLSTM with CNN Features For HAR in Videos

El reconocimiento de acciones en videos es actualmente un tema de interés en el área de visión por computadora debido a sus potenciales aplicaciones tales como indexación en multimedia, vigilancia en espacios públicos, entre otras. En este trabajo proponemos una arquitectura CNN-BiLSTM. Primero, una red neuronal convolucional VGG16 previamente entrenada extrae las características del video de entrada. Luego, un BiLSTM clasifica el video en una clase en particular. Evaluamos el rendimiento de nuestro sistema utilizando la precisión como métrica de evaluación, obteniendo 40.9% y 78.1% para los conjuntos de datos HMDB-51 y LTCF-101 respectivamente.Sociedad Argentina de Informática e Investigación Operativ

Reconocimiento de Acciones Humanas en Videos usando una Red Neuronal CNN LSTM Robusta

Action recognition in videos is currently a topic of interest in the area of computer vision, due to potential applications such as: multimedia indexing, surveillance in public spaces, among others. In this paper we propose (1) The implementation of a CNN–LSTM architecture. First, a pre-trained VGG16 convolutional neural network extracts the features of the input video. Then, an LSTM classifies the video sequence in a particular class. (2) A study of how the number of LSTM units affects the performance of the system. To carry out the training and test phases, we used the KTH, UCF-11 and HMDB-51 datasets. (3) An evaluation of the performance of our system using accuracy as evaluation metric, given the existing balance of the classes in the datasets. We obtain 93%, 91% and 47% accuracy respectively for each dataset, improving state of the art results for the former two. Besides the results attained, the main contribution of this work lays on the evaluation of different CNN-LSTM architectures for the action recognition task.El reconocimiento de acciones en videos es actualmente un tema de interés en el área de visión por computadora, debido a potenciales aplicaciones como: indexación multimedia, vigilancia en espacios públicos, entre otras. En este artículo proponemos: (1) Implementar una arquitectura CNN–LSTM para esta tarea. Primero, una red neuronal convolucional VGG16 previamente entrenada extrae las características del video de entrada. Luego, una capa LSTM determina la clase particular del video. (2) Estudiar cómo la cantidad de unidades LSTM afecta el rendimiento del sistema. Para llevar a cabo las fases de entrenamiento y prueba, utilizamos los conjuntos de datos KTH, UCF-11 y HMDB-51. (3) Evaluar el rendimiento de nuestro sistema utilizando la precisión como métrica de evaluación, dado el balance existente entre las clases de los conjuntos de datos. Obtenemos un 93%, 91% y 47% de precisión respectivamente para cada conjunto de datos, mejorando los resultados del estado del arte para los primeros dos. Además de los resultados obtenidos, la principal contribución de este trabajo yace en la evaluación de diferentes arquitecturas CNN-LSTM para la tarea de reconocimiento de acciones

Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader