Embedding Robotic Agents in the Social Environment

This paper discusses the interactive vision approach, which advocates using knowledge from the human sciences on the structure and dynamics of human-human interaction in the development of machine vision systems and interactive robots. While this approach is discussed generally, the particular case of the system being developed for the Aurora project (which aims to produce a robot to be used as a tool in the therapy of children with autism) is especially considered, with description of the design of the machine vision system being employed and discussion of ideas from the human sciences with particular reference to the Aurora system. An example architecture for a simple interactive agent, which will likely form the basis for the first implementation of this system, is briefly described and a description of hardware used for the Aurora system is given.Peer reviewe

Human mobility monitoring in very low resolution visual sensor network

This paper proposes an automated system for monitoring mobility patterns using a network of very low resolution visual sensors (30 30 pixels). The use of very low resolution sensors reduces privacy concern, cost, computation requirement and power consumption. The core of our proposed system is a robust people tracker that uses low resolution videos provided by the visual sensor network. The distributed processing architecture of our tracking system allows all image processing tasks to be done on the digital signal controller in each visual sensor. In this paper, we experimentally show that reliable tracking of people is possible using very low resolution imagery. We also compare the performance of our tracker against a state-of-the-art tracking method and show that our method outperforms. Moreover, the mobility statistics of tracks such as total distance traveled and average speed derived from trajectories are compared with those derived from ground truth given by Ultra-Wide Band sensors. The results of this comparison show that the trajectories from our system are accurate enough to obtain useful mobility statistics

Traffic monitoring using image processing : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Telecommunications Engineering at Massey University, Palmerston North, New Zealand

Traffic monitoring involves the collection of data describing the characteristics of vehicles and their movements. Such data may be used for automatic tolls, congestion and incident detection, law enforcement, and road capacity planning etc. With the recent advances in Computer Vision technology, videos can be analysed automatically and relevant information can be extracted for particular applications. Automatic surveillance using video cameras with image processing technique is becoming a powerful and useful technology for traffic monitoring. In this research project, a video image processing system that has the potential to be developed for real-time application is developed for traffic monitoring including vehicle tracking, counting, and classification. A heuristic approach is applied in developing this system. The system is divided into several parts, and several different functional components have been built and tested using some traffic video sequences. Evaluations are carried out to show that this system is robust and can be developed towards real-time applications

The 3D laser radar vision processor system

Loral Defense Systems (LDS) developed a 3D Laser Radar Vision Processor system capable of detecting, classifying, and identifying small mobile targets as well as larger fixed targets using three dimensional laser radar imagery for use with a robotic type system. This processor system is designed to interface with the NASA Johnson Space Center in-house Extra Vehicular Activity (EVA) Retriever robot program and provide to it needed information so it can fetch and grasp targets in a space-type scenario

A Computer-Aided Training (CAT) System for Short Track Speed Skating

Short track speed skating has become popular all over the world. The demands of a computer-aided training (CAT) system are booming due to this fact. However, the existing commercial systems for sports are highly dependent on expensive equipment and complicated hardware calibration. This dissertation presents a novel CAT system for tracking multiple skaters in short track skating competitions. Aiming at the challenges, we utilize global rink information to compensate camera motion and obtain the global spatial information of skaters; apply Random Forest to fuse multiple cues and predict the blobs for each of the skaters; and finally develop a silhouette and edge-based template matching and blob growing method to allocate each blob to corresponding skaters. The proposed multiple skaters tracking algorithm organically integrates multi-cue fusion, dynamic appearance modeling, machine learning, etc. to form an efficient and robust CAT system. The effectiveness and robustness of the proposed method are presented through experiments

Vision-Based Finger Detection, Tracking, and Event Identification Techniques for Multi-Touch Sensing and Display Systems

This study presents efficient vision-based finger detection, tracking, and event identification techniques and a low-cost hardware framework for multi-touch sensing and display applications. The proposed approach uses a fast bright-blob segmentation process based on automatic multilevel histogram thresholding to extract the pixels of touch blobs obtained from scattered infrared lights captured by a video camera. The advantage of this automatic multilevel thresholding approach is its robustness and adaptability when dealing with various ambient lighting conditions and spurious infrared noises. To extract the connected components of these touch blobs, a connected-component analysis procedure is applied to the bright pixels acquired by the previous stage. After extracting the touch blobs from each of the captured image frames, a blob tracking and event recognition process analyzes the spatial and temporal information of these touch blobs from consecutive frames to determine the possible touch events and actions performed by users. This process also refines the detection results and corrects for errors and occlusions caused by noise and errors during the blob extraction process. The proposed blob tracking and touch event recognition process includes two phases. First, the phase of blob tracking associates the motion correspondence of blobs in succeeding frames by analyzing their spatial and temporal features. The touch event recognition process can identify meaningful touch events based on the motion information of touch blobs, such as finger moving, rotating, pressing, hovering, and clicking actions. Experimental results demonstrate that the proposed vision-based finger detection, tracking, and event identification system is feasible and effective for multi-touch sensing applications in various operational environments and conditions

Real-time 3d person tracking and dense stereo maps using GPU acceleration

Interfacing with a computer, especially when interacting with a virtual three di- mensional (3D) scene, found in video games for example, can be frustrating when using only a mouse and keyboard. Recent work has been focused on alternative modes of interactions, including 3D tracking of the human body. One of the essential steps in this process is acquiring depth information of the scene. Stereo vision is the process of using two separate images of the same scene, taken from slightly different positions, to get a three dimensional view of the scene. One of the largest issues with dense stereo map generation is the high processor usage, usually preventing this process from being done in real time. In order to solve this problem, this project attempts to move the bulk of the processing to the GPU. The depth map extraction is done by matching points between the images, and using the difference in their positions to determine the depth, using multiple passes in a series of openGL vertex and fragment shaders. Once a depth map has been created, the software uses it to track a person’s movement and pose in three dimensions, by tracking key points on the person across frames, and using the depth map to find the third dimension