Effect of Neural Network on Reduction of Noise for Edge Detection

Processing photographic images is important in many applications, among them the development of automated driver assistance systems (ADAS) and autonomous vehicles. Many techniques are used for processing images, including neural networks, other types of machine learning, and edge detection. One common issue with processing these photos is the presence of noise, whether caused by the camera itself or by physical conditions (e.g., weather conditions or dirt on road signs). In this paper, a neural network is used for noise reduction to improve edge detection results and tested with two kinds of noise, Gaussian and salt & pepper noise, and three different edge detection algorithms, Canny, Sobel, and Zhang. Results showed that the noise reduction process was effective in improving performance of the edge detection process, with the exception of conditions where the noise was originally very minimal

Reconfigurable FPGA Architecture for Computer Vision Applications in Smart Camera Networks

Smart Camera Networks (SCNs) is nowadays an emerging research field which represents the natural evolution of centralized computer vision applications towards full distributed and pervasive systems. In this vision, one of the biggest effort is in the definition of a flexible and reconfigurable SCN node architecture able to remotely update the application parameter and the performed computer vision application at run­time. In this respect, we present a novel SCN node architecture based on a device in which a microcontroller manage all the network functionality as well as the remote configuration, while an FPGA implements all the necessary module of a full computer vision pipeline. In this work the envisioned architecture is first detailed in general terms, then a real implementation is presented to show the feasibility and the benefits of the proposed solution. Finally, performance evaluation results underline the potential of an hardware software codesign approach in reaching flexibility and reduced processing time

Real-time video scene analysis with heterogeneous processors

Field-Programmable Gate Arrays (FPGAs) and General Purpose Graphics Processing Units (GPUs) allow acceleration and real-time processing of computationally intensive computer vision algorithms. The decision to use either architecture in any application is determined by task-specific priorities such as processing latency, power consumption and algorithm accuracy. This choice is normally made at design time on a heuristic or fixed algorithmic basis; here we propose an alternative method for automatic runtime selection. In this thesis, we describe our PC-based system architecture containing both platforms; this provides greater flexibility and allows dynamic selection of processing platforms to suit changing scene priorities. Using the Histograms of Oriented Gradients (HOG) algorithm for pedestrian detection, we comprehensively explore algorithm implementation on FPGA, GPU and a combination of both, and show that the effect of data transfer time on overall processing performance is significant. We also characterise performance of each implementation and quantify tradeoffs between power, time and accuracy when moving processing between architectures, then specify the optimal architecture to use when prioritising each of these. We apply this new knowledge to a real-time surveillance application representative of anomaly detection problems: detecting parked vehicles in videos. Using motion detection and car and pedestrian HOG detectors implemented across multiple architectures to generate detections, we use trajectory clustering and a Bayesian contextual motion algorithm to generate an overall scene anomaly level. This is in turn used to select the architectures to run the compute-intensive detectors for the next frame on, with higher anomalies selecting faster, higher-power implementations. Comparing dynamic context-driven prioritisation of system performance against a fixed mapping of algorithms to architectures shows that our dynamic mapping method is 10% more accurate at detecting events than the power-optimised version, at the cost of 12W higher power consumption

Object Recognition

Vision-based object recognition tasks are very familiar in our everyday activities, such as driving our car in the correct lane. We do these tasks effortlessly in real-time. In the last decades, with the advancement of computer technology, researchers and application developers are trying to mimic the human's capability of visually recognising. Such capability will allow machine to free human from boring or dangerous jobs