ENERGY-EFFICIENT LIGHTWEIGHT ALGORITHMS FOR EMBEDDED SMART CAMERAS: DESIGN, IMPLEMENTATION AND PERFORMANCE ANALYSIS

An embedded smart camera is a stand-alone unit that not only captures images, but also includes a processor, memory and communication interface. Battery-powered, embedded smart cameras introduce many additional challenges since they have very limited resources, such as energy, processing power and memory. When camera sensors are added to an embedded system, the problem of limited resources becomes even more pronounced. Hence, computer vision algorithms running on these camera boards should be light-weight and efficient. This thesis is about designing and developing computer vision algorithms, which are aware and successfully overcome the limitations of embedded platforms (in terms of power consumption and memory usage). Particularly, we are interested in object detection and tracking methodologies and the impact of them on the performance and battery life of the CITRIC camera (embedded smart camera employed in this research). This thesis aims to prolong the life time of the Embedded Smart platform, without affecting the reliability of the system during surveillance tasks. Therefore, the reader is walked through the whole designing process, from the development and simulation, followed by the implementation and optimization, to the testing and performance analysis. The work presented in this thesis carries out not only software optimization, but also hardware-level operations during the stages of object detection and tracking. The performance of the algorithms introduced in this thesis are comparable to state-of-the-art object detection and tracking methods, such as Mixture of Gaussians, Eigen segmentation, color and coordinate tracking. Unlike the traditional methods, the newly-designed algorithms present notable reduction of the memory requirements, as well as the reduction of memory accesses per pixel. To accomplish the proposed goals, this work attempts to interconnect different levels of the embedded system architecture to make the platform more efficient in terms of energy and resource savings. Thus, the algorithms proposed are optimized at the API, middleware, and hardware levels to access the pixel information of the CMOS sensor directly. Only the required pixels are acquired in order to reduce the unnecessary communications overhead. Experimental results show that when exploiting the architecture capabilities of an embedded platform, 41.24% decrease in energy consumption, and 107.2% increase in battery-life can be accomplished. Compared to traditional object detection and tracking methods, the proposed work provides an additional 8 hours of continuous processing on 4 AA batteries, increasing the lifetime of the camera to 15.5 hours

SYSTEM-ON-A-CHIP (SOC)-BASED HARDWARE ACCELERATION FOR HUMAN ACTION RECOGNITION WITH CORE COMPONENTS

Today, the implementation of machine vision algorithms on embedded platforms or in portable systems is growing rapidly due to the demand for machine vision in daily human life. Among the applications of machine vision, human action and activity recognition has become an active research area, and market demand for providing integrated smart security systems is growing rapidly. Among the available approaches, embedded vision is in the top tier; however, current embedded platforms may not be able to fully exploit the potential performance of machine vision algorithms, especially in terms of low power consumption. Complex algorithms can impose immense computation and communication demands, especially action recognition algorithms, which require various stages of preprocessing, processing and machine learning blocks that need to operate concurrently. The market demands embedded platforms that operate with a power consumption of only a few watts. Attempts have been mad to improve the performance of traditional embedded approaches by adding more powerful processors; this solution may solve the computation problem but increases the power consumption. System-on-a-chip eld-programmable gate arrays (SoC-FPGAs) have emerged as a major architecture approach for improving power eciency while increasing computational performance. In a SoC-FPGA, an embedded processor and an FPGA serving as an accelerator are fabricated in the same die to simultaneously improve power consumption and performance. Still, current SoC-FPGA-based vision implementations either shy away from supporting complex and adaptive vision algorithms or operate at very limited resolutions due to the immense communication and computation demands. The aim of this research is to develop a SoC-based hardware acceleration workflow for the realization of advanced vision algorithms. Hardware acceleration can improve performance for highly complex mathematical calculations or repeated functions. The performance of a SoC system can thus be improved by using hardware acceleration method to accelerate the element that incurs the highest performance overhead. The outcome of this research could be used for the implementation of various vision algorithms, such as face recognition, object detection or object tracking, on embedded platforms. The contributions of SoC-based hardware acceleration for hardware-software codesign platforms include the following: (1) development of frameworks for complex human action recognition in both 2D and 3D; (2) realization of a framework with four main implemented IPs, namely, foreground and background subtraction (foreground probability), human detection, 2D/3D point-of-interest detection and feature extraction, and OS-ELM as a machine learning algorithm for action identication; (3) use of an FPGA-based hardware acceleration method to resolve system bottlenecks and improve system performance; and (4) measurement and analysis of system specications, such as the acceleration factor, power consumption, and resource utilization. Experimental results show that the proposed SoC-based hardware acceleration approach provides better performance in terms of the acceleration factor, resource utilization and power consumption among all recent works. In addition, a comparison of the accuracy of the framework that runs on the proposed embedded platform (SoCFPGA) with the accuracy of other PC-based frameworks shows that the proposed approach outperforms most other approaches

Computationally Efficient Target Classification in Multispectral Image Data with Deep Neural Networks

Detecting and classifying targets in video streams from surveillance cameras is a cumbersome, error-prone and expensive task. Often, the incurred costs are prohibitive for real-time monitoring. This leads to data being stored locally or transmitted to a central storage site for post-incident examination. The required communication links and archiving of the video data are still expensive and this setup excludes preemptive actions to respond to imminent threats. An effective way to overcome these limitations is to build a smart camera that transmits alerts when relevant video sequences are detected. Deep neural networks (DNNs) have come to outperform humans in visual classifications tasks. The concept of DNNs and Convolutional Networks (ConvNets) can easily be extended to make use of higher-dimensional input data such as multispectral data. We explore this opportunity in terms of achievable accuracy and required computational effort. To analyze the precision of DNNs for scene labeling in an urban surveillance scenario we have created a dataset with 8 classes obtained in a field experiment. We combine an RGB camera with a 25-channel VIS-NIR snapshot sensor to assess the potential of multispectral image data for target classification. We evaluate several new DNNs, showing that the spectral information fused together with the RGB frames can be used to improve the accuracy of the system or to achieve similar accuracy with a 3x smaller computation effort. We achieve a very high per-pixel accuracy of 99.1%. Even for scarcely occurring, but particularly interesting classes, such as cars, 75% of the pixels are labeled correctly with errors occurring only around the border of the objects. This high accuracy was obtained with a training set of only 30 labeled images, paving the way for fast adaptation to various application scenarios.Comment: Presented at SPIE Security + Defence 2016 Proc. SPIE 9997, Target and Background Signatures I

Review of computer vision in intelligent environment design

This paper discusses and compares the use of vision based and non-vision based technologies in developing intelligent environments. By reviewing the related projects that use vision based techniques in intelligent environment design, the achieved functions, technical issues and drawbacks of those projects are discussed and summarized, and the potential solutions for future improvement are proposed, which leads to the prospective direction of my PhD research

A Cyber-Physical System for integrated remote control andprotection of smart grid critical infrastructures

This work proposes a Cyber-Physical System (CPS) for protecting Smart Electric Grid Critical Infrastructures (CI) using video surveillance while remotely monitoring them. Due to the critical nature of Smart Grid, it is necessary to guarantee an adequate level of safety, security and reliability. Thus, this CPS is back-boned by a Time-Sensitive Network solution (TSN) providing concurrent support for smart-video surveillance and Smart Grid control over a single communication infrastructure. To this end, TSN delivers high-bandwidth communication for video surveil-lance and deterministic Quality of Service (QoS), latency and bandwidth guarantees, required by the time-critical Smart Grid control. On the one hand, the CPS utilizes High-availability Seamless Redundancy (HSR) in the control subsystem via Remote Terminal Units (RTU) guaranteeing seamless failover against failures in Smart Grid. On the other hand, the smart video surveillance subsystem applies machine learning to monitor secured perimeters and detect people around the Smart Grid CI. Moreover, it is also able to directly interoperate with RTUs via MODBUS protocol to send alarms in case of e.g. intrusion. The work evaluates the accuracy and performance of the detection using common metrics in surveillance field. An integrated monitoring dashboard has also been developed in which all CPS information is available in real timeThis work was partially supported by the EU Project FitOptiVis [3] through the ECSEL Joint Undertaking under GA n. 783162, a Spanish National grant funded by MINECO through APCIN PCI2018-093184, and partially by the Research Network RED2018-102511-

Performance and energy-efficient implementation of a smart city application on FPGAs

The continuous growth of modern cities and the request for better quality of life, coupled with the increased availability of computing resources, lead to an increased attention to smart city services. Smart cities promise to deliver a better life to their inhabitants while simultaneously reducing resource requirements and pollution. They are thus perceived as a key enabler to sustainable growth. Out of many other issues, one of the major concerns for most cities in the world is traffic, which leads to a huge waste of time and energy, and to increased pollution. To optimize traffic in cities, one of the first steps is to get accurate information in real time about the traffic flows in the city. This can be achieved through the application of automated video analytics to the video streams provided by a set of cameras distributed throughout the city. Image sequence processing can be performed both peripherally and centrally. In this paper, we argue that, since centralized processing has several advantages in terms of availability, maintainability and cost, it is a very promising strategy to enable effective traffic management even in large cities. However, the computational costs are enormous, and thus require an energy-efficient High-Performance Computing approach. Field Programmable Gate Arrays (FPGAs) provide comparable computational resources to CPUs and GPUs, yet require much lower amounts of energy per operation (around 6$$\times$$ and 10$$\times$$ for the application considered in this case study). They are thus preferred resources to reduce both energy supply and cooling costs in the huge datacenters that will be needed by Smart Cities. In this paper, we describe efficient implementations of high-performance algorithms that can process traffic camera image sequences to provide traffic flow information in real-time at a low energy and power cost

Image and Information Fusion Experiments with a Software-Defined Multi-Spectral Imaging System for Aviation and Marine Sensor Networks

The availability of Internet, line-of-sight and satellite identification and surveillance information as well as low-power, low-cost embedded systems-on-a-chip and a wide range of visible to long-wave infrared cameras prompted Embry Riddle Aeronautical University to collaborate with the University of Alaska Arctic Domain Awareness Center (ADAC) in summer 2016 to prototype a camera system we call the SDMSI (Software-Defined Multi-spectral Imager). The concept for the camera system from the start has been to build a sensor node that is drop-in-place for simple roof, marine, pole-mount, or buoy-mounts. After several years of component testing, the integrated SDMSI is now being tested, first on a roof-mount at Embry Riddle Prescott. The roof-mount testing demonstrates simple installation for the high spatial, temporal and spectral resolution SDMSI. The goal is to define and develop software and systems technology to complement satellite remote sensing and human monitoring of key resources such as drones, aircraft and marine vessels in and around airports, roadways, marine ports and other critical infrastructure. The SDMSI was installed at Embry Riddle Prescott in fall 2016 and continuous recording of long-wave infrared and visible images have been assessed manually and compared to salient object detection to automatically record only frames containing objects of interest (e.g. aircraft and drones). It is imagined that ultimately users of the SDMSI can pair with it via wireless to browse salient images. Further, both ADS-B (Automatic Dependent Surveillance-Broadcast) and S-AIS (Satellite Automatic Identification System) data are envisioned to be used by the SDMSI to form expectations for observing in future tests. This paper presents the preliminary results of several experiments and compares human review with smart image processing in terms of the receiver-operator characteristic. The system design and software are open architecture, such that other researchers are encouraged to construct and participate in sharing results and networking identical or improved versions of the SDMSI for safety, security and drop-in-place scientific image sensor networking

Performance and energy-efficient implementation of a smart city application on FPGAs

The continuous growth of modern cities and the request for better quality of life, coupled with the increased availability of computing resources, lead to an increased attention to smart city services. Smart cities promise to deliver a better life to their inhabitants while simultaneously reducing resource requirements and pollution. They are thus perceived as a key enabler to sustainable growth. Out of many other issues, one of the major concerns for most cities in the world is traffic, which leads to a huge waste of time and energy, and to increased pollution. To optimize traffic in cities, one of the first steps is to get accurate information in real time about the traffic flows in the city. This can be achieved through the application of automated video analytics to the video streams provided by a set of cameras distributed throughout the city. Image sequence processing can be performed both peripherally and centrally. In this paper, we argue that, since centralized processing has several advantages in terms of availability, maintainability and cost, it is a very promising strategy to enable effective traffic management even in large cities. However, the computational costs are enormous, and thus require an energy-efficient High-Performance Computing approach. Field Programmable Gate Arrays (FPGAs) provide comparable computational resources to CPUs and GPUs, yet require much lower amounts of energy per operation (around 6 × and 10 × for the application considered in this case study). They are thus preferred resources to reduce both energy supply and cooling costs in the huge datacenters that will be needed by Smart Cities. In this paper, we describe efficient implementations of high-performance algorithms that can process traffic camera image sequences to provide traffic flow information in real-time at a low energy and power cost