Egocentric Activity Recognition Using HOG, HOF and MBH Features

recognizing egocentric actions is a challenging task that has to be addressed in recent years. The recognition of first person activities helps in assisting elderly people, disabled patients and so on. Here, life logging activity videos are taken as input. There are 2 categories, first one is the top level and second one is second level. In this research work, the recognition is done using the features like Histogram of Oriented Gradients (HOG), Histogram of optical Flow (HOF) and Motion Boundary Histogram (MBH). The extracted features are given as input to the classifiers like Support Vector Machine (SVM) and k Nearest Neighbor (kNN). The performance results showed that SVM gave better results than kNN classifier for both categories

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

Feature Extraction and Recognition for Human Action Recognition

How to automatically label videos containing human motions is the task of human action recognition. Traditional human action recognition algorithms use the RGB videos as input, and it is a challenging task because of the large intra-class variations of actions, cluttered background, possible camera movement, and illumination variations. Recently, the introduction of cost-effective depth cameras provides a new possibility to address difficult issues. However, it also brings new challenges such as noisy depth maps and time alignment. In this dissertation, effective and computationally efficient feature extraction and recognition algorithms are proposed for human action recognition. At the feature extraction step, two novel spatial-temporal feature descriptors are proposed which can be combined with local feature detectors. The first proposed descriptor is the Shape and Motion Local Ternary Pattern (SMltp) descriptor which can dramatically reduced the number of features generated by dense sampling without sacrificing the accuracy. In addition, the Center-Symmetric Motion Local Ternary Pattern (CS-Mltp) descriptor is proposed, which describes the spatial and temporal gradients-like features. Both descriptors (SMltp and CS-Mltp) take advantage of the Local Binary Pattern (LBP) texture operator in terms of tolerance to illumination change, robustness in homogeneous region and computational efficiency. For better feature representation, this dissertation presents a new Dictionary Learning (DL) method to learn an overcomplete set of representative vectors (atoms) so that any input feature can be approximated by a linear combination of these atoms with minimum reconstruction error. Instead of simultaneously learning one overcomplete dictionary for all classes, we learn class-specific sub-dictionaries to increase the discrimination. In addition, the group sparsity and the geometry constraint are added to the learning process to further increase the discriminative power, so that features are well reconstructed by atoms from the same class and features from the same class with high similarity will be forced to have similar coefficients. To evaluate the proposed algorithms, three applications including single view action recognition, distributed multi-view action recognition, and RGB-D action recognition have been explored. Experimental results on benchmark datasets and comparative analyses with the state-of-the-art methods show the effectiveness and merits of the proposed algorithms

Feature tracking and motion compensation for action recognition

This paper discusses an approach to human action recognition via local feature tracking and robust estimation of background motion. The main contribution is a robust feature extraction algorithm based on KLT tracker and SIFT as well as a method for estimating dominant planes in the scene. Multiple interest point detectors are used to provide large number of features for every frame. The motion vectors for the features are estimated using optical flow and SIFT based matching. The features are combined with image segmentation to estimate dominant homographies, and then separated into static and moving ones regardless the camera motion. The action recognition approach can handle camera motion, zoom, human appearance variations, background clutter and occlusion. The motion compensation shows very good accuracy on a number of test sequences. The recognition system is extensively compared to state-of-the art action recognition methods and the results are improved.

Перспективы развития фундаментальных наук: сборник научных трудов XI Международной конференция студентов и молодых ученых, г. Томск, 22-25 апреля 2014 г.

Сборник содержит труды участников XI Международной конференции студентов и молодых учёных "Перспективы развития фундаментальных наук". Включает доклады студентов и молодых ученых, представленные на секциях "физика", "химия", "математика", "технология", наноматериалы и нанотехнологии», "IT-технологии и электроника". В рамках секций представлены доклады студентов представленные для соискания стипендий по программе У.М.Н.И.К. Сборник представляет интерес для студентов, аспирантов, молодых ученых, преподавателей в области естественных наук и высшей математик

Перспективы развития фундаментальных наук: сборник научных трудов XI Международной конференция студентов и молодых ученых, г. Томск, 22-25 апреля 2014 г.

Сборник содержит труды участников XI Международной конференции студентов и молодых учёных "Перспективы развития фундаментальных наук". Включает доклады студентов и молодых ученых, представленные на секциях "физика", "химия", "математика", "технология", наноматериалы и нанотехнологии», "IT-технологии и электроника". В рамках секций представлены доклады студентов представленные для соискания стипендий по программе У.М.Н.И.К. Сборник представляет интерес для студентов, аспирантов, молодых ученых, преподавателей в области естественных наук и высшей математик