Descriptive temporal template features for visual motion recognition
نویسندگان
چکیده
0167-8655/$ see front matter 2009 Elsevier B.V. A doi:10.1016/j.patrec.2009.03.003 * Corresponding author. Tel.: +44 1522 88 6974. E-mail address: [email protected] (H. Me In this paper, a human action recognition system is proposed. The system is based on new, descriptive ‘temporal template’ features in order to achieve high-speed recognition in real-time, embedded applications. The limitations of the well-known ‘Motion History Image’ (MHI) temporal template are addressed and a new ‘Motion History Histogram’ (MHH) feature is proposed to capture more motion information in the video. MHH not only provides rich motion information, but also remains computationally inexpensive. To further improve classification performance, we combine both MHI and MHH into a low dimensional feature vector which is processed by a support vector machine (SVM). Experimental results show that our new representation can achieve a significant improvement in the performance of human action recognition over existing comparable methods, which use 2D temporal template based representations. 2009 Elsevier B.V. All rights reserved.
منابع مشابه
Recognition of Visual Events using Spatio-Temporal Information of the Video Signal
Recognition of visual events as a video analysis task has become popular in machine learning community. While the traditional approaches for detection of video events have been used for a long time, the recently evolved deep learning based methods have revolutionized this area. They have enabled event recognition systems to achieve detection rates which were not reachable by traditional approac...
متن کاملMiddle-Level Representation for Human Activities Recognition: The Role of Spatio-Temporal Relationships
We tackle the challenging problem of human activity recognition in realistic video sequences. Unlike local features-based methods or global template-based methods, we propose to represent a video sequence by a set of middle-level parts. A part, or component, has consistent spatial structure and consistent motion. We first segment the visual motion patterns and generate a set of middle-level com...
متن کاملVoiceless Speech Recognition Using Dynamic Visual Speech Features
This paper describes a voiceless speech recognition technique that utilizes dynamic visual features to represent the facial movements during phonation. The dynamic features extracted from the mouth video are used to classify utterances without using the acoustic data. The audio signals of consonants are more confusing than vowels and the facial movements involved in pronunciation of consonants ...
متن کاملEMG-based wrist gesture recognition using a convolutional neural network
Background: Deep learning has revolutionized artificial intelligence and has transformed many fields. It allows processing high-dimensional data (such as signals or images) without the need for feature engineering. The aim of this research is to develop a deep learning-based system to decode motor intent from electromyogram (EMG) signals. Methods: A myoelectric system based on convolutional ne...
متن کاملDictionary-Based Lip Reading Classification
Visual lip reading recognition is an essential stage in many multimedia systems such as “Audio Visual Speech Recognition” [6], “Mobile Phone Visual System for deaf people”, “Sign Language Recognition System”, etc. The use of lip visual features to help audio or hand recognition is appropriate because this information is robust to acoustic noise. In this paper, we describe our work towards devel...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Pattern Recognition Letters
دوره 30 شماره
صفحات -
تاریخ انتشار 2009