http://pancamr.lecture.ub.ac.id/files/2012/02/A-survey-on-vision-based-human-action-recognition_Image-and-Vision-Computing_2010.pdf

A survey on vision-based human action recognition

The capacity to estimate the head pose of another person is a common human ability that presents a unique challenge for computer vision systems. Compared to face detection and recognition, which have been the primary foci of face-related vision research, identity-invariant head pose estimation has fewer rigorously evaluated systems or generic solutions. In this paper, we discuss the inherent difficulties in head pose estimation and present an organized survey describing the evolution of the field. Our discussion focuses on the advantages and disadvantages of each approach and spans 90 of the most innovative and characteristic papers that have been published on this topic. We compare these systems by focusing on their ability to estimate coarse and fine head pose, highlighting approaches that are well suited for unconstrained environments.

Head Pose Estimation in Computer Vision: A Survey

Advanced driver assistance systems (ADASs), and particularly pedestrian protection systems (PPSs), have become an active research area aimed at improving traffic safety. The major challenge of PPSs is the development of reliable on-board pedestrian detection systems. Due to the varying appearance of pedestrians (e.g., different clothes, changing size, aspect ratio, and dynamic shape) and the unstructured environment, it is very difficult to cope with the demanded robustness of this kind of system. Two problems arising in this research area are the lack of public benchmarks and the difficulty in reproducing many of the proposed methods, which makes it difficult to compare the approaches. As a result, surveying the literature by enumerating the proposals one--after-another is not the most useful way to provide a comparative point of view. Accordingly, we present a more convenient strategy to survey the different approaches. We divide the problem of detecting pedestrians from images into different processing steps, each with attached responsibilities. Then, the different proposed methods are analyzed and classified with respect to each processing stage, favoring a comparative viewpoint. Finally, discussion of the important topics is presented, putting special emphasis on the future needs and challenges.

http://www.cvc.uab.es/~asappa/publications/J__IEEE_PAMI_vol_32_Issue_7_July_2010.pdf

Survey of Pedestrian Detection for Advanced Driver Assistance Systems

Thermal cameras are passive sensors that capture the infrared radiation emitted by all objects with a temperature above absolute zero. This type of camera was originally developed as a surveillance and night vision tool for the military, but recently the price has dropped, significantly opening up a broader field of applications. Deploying this type of sensor in vision systems eliminates the illumination problems of normal greyscale and RGB cameras. This survey provides an overview of the current applications of thermal cameras. Applications include animals, agriculture, buildings, gas detection, industrial, and military applications, as well as detection, tracking, and recognition of humans. Moreover, this survey describes the nature of thermal radiation and the technology of thermal cameras.

/pdf/thermal-cameras-and-applications-a-survey-4wd1xvg4uy.pdf

Thermal cameras and applications: a survey

This paper describes the recent research on the enhancement of pedestrian safety to help develop a better understanding of the nature, issues, approaches, and challenges surrounding the problem. It presents a comprehensive review of research efforts underway dealing with pedestrian safety and collision avoidance. The importance of pedestrian protection is emphasized in a global context, discussing the research programs and efforts in various countries. Pedestrian safety measures, including infrastructure enhancements and passive safety features in vehicles, are described, followed by a systematic description of active safety systems based on pedestrian detection using sensors in vehicle and infrastructure. The pedestrian detection approaches are classified according to various criteria such as the type and configuration of sensors, as well as the video cues and classifiers used in detection algorithms. It is noted that collision avoidance not only requires detection of pedestrians but also requires collision prediction using pedestrian dynamics and behavior analysis. Hence, this paper includes research dealing with probabilistic modeling of pedestrian behavior for predicting collisions between pedestrians and vehicles.

https://cvrr.ucsd.edu/publications/2007/TGandhi_ITS07_pedestrian.pdf

Pedestrian Protection Systems: Issues, Survey, and Challenges

http://cvrr.ucsd.edu/publications/2007/SCheng_CVIU07.pdf

Multi-spectral and multi-perspective video arrays for driver body tracking and activity analysis

This paper presents a multi-perspective (i.e., four camera views) multi-modal (i.e., thermal infrared and color) video based system for robust and real-time 3D tracking of important body parts.The multi-perspective characteristics of the system provides 3Dtrajectory of the body parts, while the multi-modal characteristics of the system provides robustness and reliability of feature detection and tracking. The application context for this research is that of intelligent vehicles and driver assistance systems. Experimental results demonstrate effectiveness of the proposed system.

http://cvrr.ucsd.edu/publications/2005/Cheng_OTCBVS05_online.pdf

Multiperspective Thermal IR and Video Arrays for 3D Body Tracking and Driver Activity Analysis

This paper presents a synergistic track- and body-level analysis framework for multi-person interaction and activity analysis in the context of video surveillance. The proposed two-level analysis framework covers human activities both in wide and narrow fields of view with distributed camera sensors. The track-level analysis deals with the gross-level activity patterns of multiple tracks in various wide-area surveillance situations. The body-level analysis focuses on detailed-level activity patterns of individuals in isolation or in groups. ‘Spatio-temporal personal space’ is introduced to model various patterns of grouping behavior between persons. ‘Adaptive context switching’ is proposed to mediate the track-level and body-level analysis depending on the interpersonal configuration and imaging fidelity. Our approach is based on the hierarchy of action concepts: static pose, dynamic gesture, body-part action, single-person activity, and group interaction. Event ontology with human activity hierarchy combines the multi-level analysis results to form a semantically meaningful event description. Experimental results with real-world data show the effectiveness of the proposed framework.

http://cvrr.ucsd.edu/publications/2007/Park_MVA2007_Multi_person_.pdf

Multi-person interaction and activity analysis: a synergistic track- and body-level analysis framework

This paper examines the feasibility of a semantic-level driver activity analysis system. Several new considerations are made to construct the hierarchy of driver activity. Driver activity is represented and recognized at multiple levels: individual body-part pose/gesture at the low level, single body-part action at the middle level, and the driver interaction with the vehicle at the high level. Driving is represented in terms of the interactions among driver, vehicle, and surround, and driver activity is recognized by a rule-based decision tree. Our system works with a single color camera data, and it can be easily expanded to incorporate multimodal sensor data.

Sangho Park

Papers

Multi-spectral and multi-perspective video arrays for driver body tracking and activity analysis

Multiperspective Thermal IR and Video Arrays for 3D Body Tracking and Driver Activity Analysis

Multi-person interaction and activity analysis: a synergistic track- and body-level analysis framework

Driver activity analysis for intelligent vehicles: issues and development framework

Understanding human interactions with track and body synergies (TBS) captured from multiple views