Julien Nyambal - Academia.edu (original) (raw)
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Papers by Julien Nyambal
Finding a parking space nowadays becomes an issue that is not to be neglected, it consumes time a... more Finding a parking space nowadays becomes an issue that is not to be neglected, it consumes time and energy. We have used computer vision techniques to infer the state of the parking lot given the data collected from the University of The Witwatersrand. This paper presents an approach for a realtime parking space classification based on Convolutional Neural Networks (CNN) using Caffe and Nvidia DiGITS framework. The training process has been done using DiGITS and the output is a caffemodel used for predictions to detect vacant and occupied parking spots. The system checks a defined area whether a parking spot (bounding boxes defined at initialization of the system) is containing a car or not (occupied or vacant). Those bounding box coordinates are saved from a frame of the video of the parking lot in a JSON format, to be later used by the system for sequential prediction on each parking spot. The system has been trained using the LeNet network with the Nesterov Accelerated Gradient as solver and the AlexNet network with the Stochastic Gradient Descent as solver. We were able to get an accuracy on the validation set of 99% for both networks. The accuracy on a foreign dataset(PKLot) returned as well 99%. Those are experimental results based on the training set shows how robust the system can be when the prediction has to take place in a different parking space.
A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfillme... more A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfillment of the requirements for the degree of Master of Science. Johannesburg, 2018.Parking space management is a problem that most big cities encounter. Without parking space management strategies, the traffic can become anarchic. Compared to physical sensors around the parking lot, a camera monitoring it can send images to be processed for vacancy detection. This dissertation implements a system to automatically detect and classify spaces (vacant or occupied) in images of a parking lot. Detection is done using the Region based Convolutional Neural Networks (RCNN). It reduces the amount of time that would otherwise be spent manually mapping out a parking lot. After the spaces are detected, they are classified as either vacant or occupied. It is accomplished using the Histograms of Oriented Gradients (HOG) with the Linear and Radial Basis Function (RBF) Support Vector Machines (SVM), Convolut...
2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech)
Finding a parking space nowadays becomes an issue that is not to be neglected, it consumes time a... more Finding a parking space nowadays becomes an issue that is not to be neglected, it consumes time and energy. We have used computer vision techniques to infer the state of the parking lot given the data collected from the University of The Witwatersrand. This paper presents an approach for a realtime parking space classification based on Convolutional Neural Networks (CNN) using Caffe and Nvidia DiGITS framework. The training process has been done using DiGITS and the output is a caffemodel used for predictions to detect vacant and occupied parking spots. The system checks a defined area whether a parking spot (bounding boxes defined at initialization of the system) is containing a car or not (occupied or vacant). Those bounding box coordinates are saved from a frame of the video of the parking lot in a JSON format, to be later used by the system for sequential prediction on each parking spot. The system has been trained using the LeNet network with the Nesterov Accelerated Gradient as solver and the AlexNet network with the Stochastic Gradient Descent as solver. We were able to get an accuracy on the validation set of 99% for both networks. The accuracy on a foreign dataset(PKLot) returned as well 99%. Those are experimental results based on the training set shows how robust the system can be when the prediction has to take place in a different parking space.
A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfillme... more A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfillment of the requirements for the degree of Master of Science. Johannesburg, 2018.Parking space management is a problem that most big cities encounter. Without parking space management strategies, the traffic can become anarchic. Compared to physical sensors around the parking lot, a camera monitoring it can send images to be processed for vacancy detection. This dissertation implements a system to automatically detect and classify spaces (vacant or occupied) in images of a parking lot. Detection is done using the Region based Convolutional Neural Networks (RCNN). It reduces the amount of time that would otherwise be spent manually mapping out a parking lot. After the spaces are detected, they are classified as either vacant or occupied. It is accomplished using the Histograms of Oriented Gradients (HOG) with the Linear and Radial Basis Function (RBF) Support Vector Machines (SVM), Convolut...
2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics (PRASA-RobMech)