to download the abstract project of android

We provide android project in this project. Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, human-computer interaction (HCI) and real-world applications. This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research in android.
Human activity recognition, real world applications

Human activity recognition (HAR) is a widely studied computer vision problem. Applications of HAR include video surveillance, health care, and human-computer interaction. As the imaging technique advances and the camera device upgrades, novel approaches for HAR constantly emerge. This review aims to provide a comprehensive introduction to the video-based human activity recognition, giving an overview of various approaches as well as their evolutions by covering both the representative classical
literatures and the state-of-the-art approaches. Human activities have an inherent hierarchical structure that indicates the different levels of it, which can be considered as a three-level categorization. First, for the bottom level, there is an atomic element and these action primitives constitute more complex human
activities. After the action primitive level, the action/activity comes as the second level. Finally, the complex interactions form the top level, which refers to the human activities that involve more than two persons and objects. In this paper, we follow this three-level categorization namely action primitives, actions/activities, and interactions. This three level categorization varies a little from previous surveys and maintains a consistent theme. Action primitives are those atomic actions at the limb level, such as “stretching the left arm,” and “raising the right leg.” Atomic actions are performed by a specific part of the
human body, such as the hands, arms, or upper body part. Actions and activities are used interchangeably in this review, referring to the whole-body movements composed of several action primitives in temporal sequential order and performed by a single person with no more person or additional objects. Specifically, we refer the terminology human activities as all movements of the three layers and the activities/actions as the middle level of human activities. Human activities like walking, running, and waving hands are categorized in the actions/activities level. Finally, similar to Aggarwal et al.’s review,
interactions are human activities that involve two or more persons and objects. The additional person or object is an important characteristic of interaction. Typical examples of interactions are cooking which involves one person and various pots and pans and other activities that is performed by two persons.
This review highlights the advances of image representation approaches and classification methods in vision-based activity recognition. Generally, for representation approaches, related literatures follow a research trajectory of global representations, local representations, and recent depth-based representations. Earlier studies attempted to model the whole images or silhouettes and represent human activities in a global manner. The approach in is an example of global representation in which space-time shapes are generated as the image descriptors. Then, the emergency of space-time interest points (STIPs) android proposed in triggered significant attention to a new local representation view that focuses on the informative interest points. Meanwhile, local descriptors such as histogram of oriented gradients (HOG) and histogram of optical flow (HOF) oriented from object recognition are widely used or extended to 3D in HAR area. With the upgrades of camera devices, especially the launch of RGBD cameras in the year 2010, depth image-based representations have been a new research topic and have drawn growing concern in recent years.
On the other hand, classification techniques keep developing in step with machine learning methods. In fact, lots of classification methods were not originally designed for HAR. For instance, dynamic time warping (DTW) and hidden Markov model (HMM) were first used in speech recognition while the recent deep learning method is first developed for large amount image classification . To measure these approaches with same criterion, lots of activity datasets are collected, forming public and transparent benchmarks for comparing different approaches. In addition to the activity classification approaches, another critical research area within the HAR scope, the human tracking approach, is also reviewed briefly in a separate section. It is widely concerned especially in video surveillance systems for suspicious behavior detection in android.
The writing of rest parts conforms to general HAR process flow. First, research emphases and challenges of this domain are briefly illustrated in Section Then, effective features need to be designed for the representation of activity images or videos. Thus, Sections 3 and 4, respectively, review the global and local representations in conventional RGB videos. Depth image-based representations are discussed as a separate part in Section Next, Section 6 describes the classification approaches. To measure and compare
different approaches, benchmark datasets act an important role on which various approaches are evaluated. Section 7 collects recent human tracking methods of two dominant categories. In Section 8 we present representative datasets in different levels. Before we conclude this review and the future of HAR in Section 8, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the used datasets aiming at a comprehensive and convenient overview for HAR researchers in android.

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