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

Measuring traffic flow and lane changing from semi-automatic video processing

Comprehensive databases are needed in order to extend our knowledge on the behavior of vehicular traffic. Nevertheless data coming from common traffic detectors is incomplete. Detectors only provide vehicle count, detector occupancy and speed at discrete locations. To enrich these databases additional measurements from other data sources, like video recordings, are used. Extracting data from videos by actually watching the entire length of the recordings and manually counting is extremely time-consuming. The alternative is to set up an automatic video detection system. This is also costly in terms of money and time, and generally does not pay off for sporadic usage on a pilot test. An adaptation of the semi-automatic video processing methodology proposed by Patire (2010) is presented here. It makes possible to count flow and lane changes 90% faster than actually counting them by looking at the video. The method consists in selecting some specific lined pixels in the video, and converting them into a set of space – time images. The manual time is only spent in counting from these images. The method is adaptive, in the sense that the counting is always done at the maximum speed, not constrained by the video playback speed. This allows going faster when there are a few counts and slower when a lot of counts happen. This methodology has been used for measuring off-ramp flows and lane changing at several locations in the B-23 freeway (Soriguera & Sala, 2014). Results show that, as long as the video recordings fulfill some minimum requirements in framing and quality, the method is easy to use, fast and reliable. This method is intended for research purposes, when some hours of video recording have to be analyzed, not for long term use in a Traffic Management Center.Postprint (published version

Available seat counting in public rail transport

Surveillance cameras are found almost everywhere today, including vehicles for public transport. A lot of research has already been done on video analysis in open spaces. However, the conditions in a vehicle for public transport differ from these in open spaces, as described in detail in this paper. A use case described in this paper is on counting the available seats in a vehicle using surveillance cameras. We propose an algorithm based on Laplace edge detection, combined with background subtraction

FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

In this paper, we develop deep spatio-temporal neural networks to sequentially count vehicles from low quality videos captured by city cameras (citycams). Citycam videos have low resolution, low frame rate, high occlusion and large perspective, making most existing methods lose their efficacy. To overcome limitations of existing methods and incorporate the temporal information of traffic video, we design a novel FCN-rLSTM network to jointly estimate vehicle density and vehicle count by connecting fully convolutional neural networks (FCN) with long short term memory networks (LSTM) in a residual learning fashion. Such design leverages the strengths of FCN for pixel-level prediction and the strengths of LSTM for learning complex temporal dynamics. The residual learning connection reformulates the vehicle count regression as learning residual functions with reference to the sum of densities in each frame, which significantly accelerates the training of networks. To preserve feature map resolution, we propose a Hyper-Atrous combination to integrate atrous convolution in FCN and combine feature maps of different convolution layers. FCN-rLSTM enables refined feature representation and a novel end-to-end trainable mapping from pixels to vehicle count. We extensively evaluated the proposed method on different counting tasks with three datasets, with experimental results demonstrating their effectiveness and robustness. In particular, FCN-rLSTM reduces the mean absolute error (MAE) from 5.31 to 4.21 on TRANCOS, and reduces the MAE from 2.74 to 1.53 on WebCamT. Training process is accelerated by 5 times on average.Comment: Accepted by International Conference on Computer Vision (ICCV), 201

Understanding Traffic Density from Large-Scale Web Camera Data

Understanding traffic density from large-scale web camera (webcam) videos is a challenging problem because such videos have low spatial and temporal resolution, high occlusion and large perspective. To deeply understand traffic density, we explore both deep learning based and optimization based methods. To avoid individual vehicle detection and tracking, both methods map the image into vehicle density map, one based on rank constrained regression and the other one based on fully convolution networks (FCN). The regression based method learns different weights for different blocks in the image to increase freedom degrees of weights and embed perspective information. The FCN based method jointly estimates vehicle density map and vehicle count with a residual learning framework to perform end-to-end dense prediction, allowing arbitrary image resolution, and adapting to different vehicle scales and perspectives. We analyze and compare both methods, and get insights from optimization based method to improve deep model. Since existing datasets do not cover all the challenges in our work, we collected and labelled a large-scale traffic video dataset, containing 60 million frames from 212 webcams. Both methods are extensively evaluated and compared on different counting tasks and datasets. FCN based method significantly reduces the mean absolute error from 10.99 to 5.31 on the public dataset TRANCOS compared with the state-of-the-art baseline.Comment: Accepted by CVPR 2017. Preprint version was uploaded on http://welcome.isr.tecnico.ulisboa.pt/publications/understanding-traffic-density-from-large-scale-web-camera-data