A Discriminative Framework for Action Recognition Using f-HOL Features

Abstract

Inspired by the overwhelming success of Histogram of Oriented Gradients (HOG) features in many vision tasks, in this paper, we present an innovative compact feature descriptor called fuzzy Histogram of Oriented Lines (f-HOL) for action recognition, which is a distinct variant of the HOG feature descriptor. The intuitive idea of these features is based on the observation that the slide area of the human body skeleton can be viewed as a spatiotemporal 3D surface, when observing a certain action being performed in a video. The f-HOL descriptor possesses an immense competitive advantage, not only of being quite robust to small geometric transformations where the small translation and rotations make no large fluctuations in histogram values, but also of not being very sensitive under varying illumination conditions. The extracted features are then fed into a discriminative conditional model based on Latent-Dynamic Conditional random fields (LDCRFs) to learn to recognize actions from video frames. When tested on the benchmark Weizmann dataset, the proposed framework substantially supersedes most existing state-of-the-art approaches, achieving an overall recognition rate of 98.2%. Furthermore, due to its low computational demands, the framework is properly amenable for integration into real-time applications.

Computer-Aided Diagnosis of Malignant Melanoma Using Gabor-Based Entropic Features and Multilevel Neural Networks

“…Hence, the learning strategy in this case, is simply referred to as supervised learning. In the existing literature, there are several classification techniques that are exactly tuned to be reliably applicable for classifying skin lesions in dermoscopy images, such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Naïve Bayesian (NB), k-Nearest Neighbor (k-NN), and Conditional Random Fields (CRFs) [ 39 , 40 , 41 , 42 , 43 ].…”

A Novel Low Processing Time System for Criminal Activities Detection Applied to Command and Control Citizen Security Centers

“…In many cases, the detection and recognition of human actions (like criminal actions) is done by analysis of movement [16][17][18]53,54] or trajectories [12][13][14][15], which implies the processing of several video frames. Nevertheless, when the video camera is mobile, it is very difficult to carry out the trajectory or movement analysis because camera movements may introduce noise to the trajectories or movements to be analyzed.…”

“…Considering that criminal actions always have fixed gestures such as threatening the victim, it is possible to consider that this criminal action can be understood by the system as an "object". This novel application has the potential to reduce the computational cost because only one video frame will be processed, compared to other action detection methods that must analyze several video frames [12][13][14][15][16][17][18]53,54]. With this novel method in mind, we proceeded with the system design and training.…”

Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images

“…In the last decades, vision-based systems are becoming increasingly important in supporting a wide range of application areas, including environment modelling for moving cameras [1][2][3][4][5], human action and event recognition [6][7][8][9], target and object detection [10][11][12][13][14][15][16]; even in areas such as medical image analysis [17][18][19][20][21][22][23], emotion or deception recognition [24][25][26][27][28][29], and immersive rehabilitation by serious games [30][31][32][33][34][35], these systems are, now, of daily use. At the same time, the last 10 years have seen substantial improvements of small-scale Unmanned Aerial Vehicles (UAVs), hereinafter UAVs, in terms of flight time, automatic control, embedded processing, and remote transmission.…”