Motion Detection by Microcontroller for Panning Cameras

Motion detection is the first essential process in the extraction of information regarding moving objects. The approaches based on background difference are the most used with fixed cameras to perform motion detection, because of the high quality of the achieved segmentation. However, real time requirements and high costs prevent most of the algorithms proposed in literature to exploit the background difference with panning cameras in real world applications. This paper presents a new algorithm to detect moving objects within a scene acquired by panning cameras. The algorithm for motion detection is implemented on a Raspberry Pi microcontroller, which enables the design and implementation of a low-cost monitoring system.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Development of artificial neural network-based object detection algorithms for low-cost hardware devices

Finally, the fourth work was published in the “WCCI” conference in 2020 and consisted of an individuals' position estimation algorithm based on a novel neural network model for environments with forbidden regions, named “Forbidden Regions Growing Neural Gas”.The human brain is the most complex, powerful and versatile learning machine ever known. Consequently, many scientists of various disciplines are fascinated by its structures and information processing methods. Due to the quality and quantity of the information extracted from the sense of sight, image is one of the main information channels used by humans. However, the massive amount of video footage generated nowadays makes it difficult to process those data fast enough manually. Thus, computer vision systems represent a fundamental tool in the extraction of information from digital images, as well as a major challenge for scientists and engineers. This thesis' primary objective is automatic foreground object detection and classification through digital image analysis, using artificial neural network-based techniques, specifically designed and optimised to be deployed in low-cost hardware devices. This objective will be complemented by developing individuals' movement estimation methods by using unsupervised learning and artificial neural network-based models. The cited objectives have been addressed through a research work illustrated in four publications supporting this thesis. The first one was published in the “ICAE” journal in 2018 and consists of a neural network-based movement detection system for Pan-Tilt-Zoom (PTZ) cameras deployed in a Raspberry Pi board. The second one was published in the “WCCI” conference in 2018 and consists of a deep learning-based automatic video surveillance system for PTZ cameras deployed in low-cost hardware. The third one was published in the “ICAE” journal in 2020 and consists of an anomalous foreground object detection and classification system for panoramic cameras, based on deep learning and supported by low-cost hardware

Control of a PTZ camera in a hybrid vision system

In this paper, we propose a new approach to steer a PTZ camera in the direction of a detected object visible from another fixed camera equipped with a fisheye lens. This heterogeneous association of two cameras having different characteristics is called a hybrid stereo-vision system. The presented method employs epipolar geometry in a smart way in order to reduce the range of search of the desired region of interest. Furthermore, we proposed a target recognition method designed to cope with the illumination problems, the distortion of the omnidirectional image and the inherent dissimilarity of resolution and color responses between both cameras. Experimental results with synthetic and real images show the robustness of the proposed method

Development of an Active Vision System for the Remote Identification of Multiple Targets

This thesis introduces a centralized active vision system for the remote identification of multiple targets in applications where the targets may outnumber the active system resources. Design and implementation details of a modular active vision system are presented, from which a prototype has been constructed. The system employs two different, yet complimentary, camera technologies. Omnidirectional cameras are used to detect and track targets at a low resolution, while perspective cameras mounted to pan-tilt stages are used to acquire high resolution images suitable for identification. Five greedy-based scheduling policies have been developed and implemented to manage the active system resources in an attempt to achieve optimal target-to-camera assignments. System performance has been evaluated using both simulated and real-world experiments under different target and system configurations for all five scheduling policies. Parameters affecting performance that were considered include: target entry conditions, congestion levels, target to camera speeds, target trajectories, and number of active cameras. An overall trend in the relative performance of the scheduling algorithms was observed. The Least System Reconfiguration and Future Least System Reconfiguration scheduling policies performed the best for the majority of conditions investigated, while the Load Sharing and First Come First Serve policies performed the poorest. The performance of the Earliest Deadline First policy was seen to be highly dependent on target predictability

Development of an Active Vision System for the Remote Identification of Multiple Targets

This thesis introduces a centralized active vision system for the remote identification of multiple targets in applications where the targets may outnumber the active system resources. Design and implementation details of a modular active vision system are presented, from which a prototype has been constructed. The system employs two different, yet complimentary, camera technologies. Omnidirectional cameras are used to detect and track targets at a low resolution, while perspective cameras mounted to pan-tilt stages are used to acquire high resolution images suitable for identification. Five greedy-based scheduling policies have been developed and implemented to manage the active system resources in an attempt to achieve optimal target-to-camera assignments. System performance has been evaluated using both simulated and real-world experiments under different target and system configurations for all five scheduling policies. Parameters affecting performance that were considered include: target entry conditions, congestion levels, target to camera speeds, target trajectories, and number of active cameras. An overall trend in the relative performance of the scheduling algorithms was observed. The Least System Reconfiguration and Future Least System Reconfiguration scheduling policies performed the best for the majority of conditions investigated, while the Load Sharing and First Come First Serve policies performed the poorest. The performance of the Earliest Deadline First policy was seen to be highly dependent on target predictability

Toward Global Sensing Quality Maximization: A Configuration Optimization Scheme for Camera Networks

The performance of a camera network monitoring a set of targets depends crucially on the configuration of the cameras. In this paper, we investigate the reconfiguration strategy for the parameterized camera network model, with which the sensing qualities of the multiple targets can be optimized globally and simultaneously. We first propose to use the number of pixels occupied by a unit-length object in image as a metric of the sensing quality of the object, which is determined by the parameters of the camera, such as intrinsic, extrinsic, and distortional coefficients. Then, we form a single quantity that measures the sensing quality of the targets by the camera network. This quantity further serves as the objective function of our optimization problem to obtain the optimal camera configuration. We verify the effectiveness of our approach through extensive simulations and experiments, and the results reveal its improved performance on the AprilTag detection tasks. Codes and related utilities for this work are open-sourced and available at https://github.com/sszxc/MultiCam-Simulation.Comment: The 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022

Low cost network camera sensors for traffic monitoring

Report on a study investigating the ways new video and wireless technology can be implemented into Texas Department of Transportation video monitoring systems to increase efficiency and reduce costs

Omnidirectional video stabilisation on a virtual camera using sensor fusion

This paper presents a method for robustly stabilising omnidirectional video given the presence of significantrotations and translations by creating a virtual camera and using a combination of sensor fusion and scene tracking. Real time rotational movements of the camera are measured by an Inertial Measurement Unit (IMU), which provides an initial estimate of the ego-motion of the camera platform. Image registration is then used to refine these estimates. The calculated ego-motion is then used to adjust an extract of the omnidirectional video, forming a virtual camera that is focused on the scene. Experiments show the technique is effective under challenging ego-motions and overcomes deficiencies that are associated with unimodal approaches making it robust and suitable to be used in many surveillance applications