Confidence based active learning for vehicle classification in urban traffic

This paper presents a framework for confidence based active learning for vehicle classification in an urban traffic environment. Vehicles are automatically detected using an improved background subtraction algorithm using a Gaussian mixture model. A vehicle observation vector is constructed from measurement-based features and an intensity-based pyramid HOG. The output scores of a linear SVM classifier are accurately calibrated to probabilities using an interpolated dynamic bin width histogram. The confidence value of each sample is measured by its probabilities. Thus, only a small number of low confidence samples need to be identified and annotated according to their confidence. Compared to passive learning, the number of annotated samples needed for the training dataset can be reduced significantly, yielding a high accuracy classifier with low computational complexity and high efficiency. The detected vehicles are classified into four main categories: car, van, bus and motorcycle. Experimental results demonstrate the effectiveness and efficiency of our approach. The method is general enough so that it can be used in other classification problems and domains, e.g. pedestrian detection

Vision-based traffic surveys in urban environments

This paper presents a state-of-the-art, vision-based vehicle detection and type classification to perform traffic surveys from a roadside closed-circuit television camera. Vehicles are detected using background subtraction based on a Gaussian mixture model that can cope with vehicles that become stationary over a significant period of time. Vehicle silhouettes are described using a combination of shape and appearance features using an intensity-based pyramid histogram of orientation gradients (HOG). Classification is performed using a support vector machine, which is trained on a small set of hand-labeled silhouette exemplars. These exemplars are identified using a model-based preclassifier that utilizes calibrated images mapped by Google Earth to provide accurately surveyed scene geometry matched to visible image landmarks. Kalman filters track the vehicles to enable classification by majority voting over several consecutive frames. The system counts vehicles and separates them into four categories: car, van, bus, and motorcycle (including bicycles). Experiments with real-world data have been undertaken to evaluate system performance and vehicle detection rates of 96.45% and classification accuracy of 95.70% have been achieved on this data.The authors gratefully acknowledge the Royal Borough of Kingston for providing the video data. S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement nº 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander

RAI-Net: Range-Adaptive LiDAR Point Cloud Frame Interpolation Network

LiDAR point cloud frame interpolation, which synthesizes the intermediate frame between the captured frames, has emerged as an important issue for many applications. Especially for reducing the amounts of point cloud transmission, it is by predicting the intermediate frame based on the reference frames to upsample data to high frame rate ones. However, due to high-dimensional and sparse characteristics of point clouds, it is more difficult to predict the intermediate frame for LiDAR point clouds than videos. In this paper, we propose a novel LiDAR point cloud frame interpolation method, which exploits range images (RIs) as an intermediate representation with CNNs to conduct the frame interpolation process. Considering the inherited characteristics of RIs differ from that of color images, we introduce spatially adaptive convolutions to extract range features adaptively, while a high-efficient flow estimation method is presented to generate optical flows. The proposed model then warps the input frames and range features, based on the optical flows to synthesize the interpolated frame. Extensive experiments on the KITTI dataset have clearly demonstrated that our method consistently achieves superior frame interpolation results with better perceptual quality to that of using state-of-the-art video frame interpolation methods. The proposed method could be integrated into any LiDAR point cloud compression systems for inter prediction.Comment: Accepted by the IEEE International Symposium on Broadband Multimedia Systems and Broadcasting 202

Variasi Temperatur Pencampuran Terhadap Parameter Marshall Pada Campuran Lapis Aspal Beton

This study was conducted to determine the effect of temperature variations on the mixing processof the asphalt concrete AC-WC (Asphalt Concrete-Wearing Course) subtle gradations in themiddle limit and lower limit of the Marshall parameters with reference to specifications of BinaMarga, 2010.From the results of experiments conducted that the optimum asphalt content is used to middle limitusing a asphalt content of 5,7% and 6,8% for the lower limit after that mixing was done usingtemperature variation of 120 o C, 130 o C, 140 o C, 150 o C, and 160 o C.To a mixture of Laston AC-WC subtle gradations middle limit grading 5,7% asphalt contentmixing temperature using a temperature of 120 o C, 130 o C, 140 o C, 150 o C, 160 o C and still meet allstandards of marshall parameters. Ideal mixing temperature variations in the middle limit ofmixing temperature 150 o C-160 o C. While the lower limit to the level of 6,8% asphalt contentmixing temperatures between 120 o C-160 o C did not meet the specifications, because the MQ valuebelow the minimum value of 250 kg / mm

Detection, tracking and classification of vehicles in urban environments

The work presented in this dissertation provides a framework for object detection,tracking and vehicle classification in urban environment. The final aim is to produce a system for traffic flow statistics analysis. Based on level set methods and a multi-phase colour model, a general variational formulation which combines Minkowski-form distance L2 and L3 of each channel and their homogenous regions in the index is defined. The active segmentation method successfully finds whole object boundaries which include different known colours, even in very complex background situations, rather than splitting an object into several regions with different colours. For video data supplied by a nominally stationary camera, an adaptive Gaussian mixture model (GMM), with a multi-dimensional Gaussian kernel spatio-temporal smoothing transform, has been used for modeling the distribution of colour image data. The algorithm improves the segmentation performance in adverse imaging conditions. A self-adaptive Gaussian mixture model, with an online dynamical learning rate and global illumination changing factor, is proposed to address the problem of sudden change in illumination. The effectiveness of a state-of-the-art classification algorithm to categorise road vehicles for an urban traffic monitoring system using a set of measurement-based feature (BMF) and a multi-shape descriptor is investigated. Manual vehicle segmentation was used to acquire a large database of labeled vehicles form a set of MBF in combination with pyramid histogram of orientation gradient (PHOG) and edge-based PHOG features. These are used to classify the objects into four main vehicle categories: car, van (van, minivan, minibus and limousine), bus (single and double decked) and motorcycle (motorcycle and bicycle). Then, an automatic system for vehicle detection, tracking and classification from roadside CCTV is presented. The system counts vehicles and separates them into the four categories mentioned above. The GMM and shadow removal method have been used to deal with sudden illumination changes and camera vibration. A Kalman filter tracks a vehicle to enable classification by majority voting over several consecutive frames, and a level set method has been used to refine the foreground blob. Finally, a framework for confidence based active learning for vehicle classification in an urban traffic environment is presented. Only a small number of low confidence samples need to be identified and annotated according to their confidence. Compared to passive learning, the number of annotated samples needed for the training dataset can be reduced significantly, yielding a high accuracy classifier with low computational complexity and high efficiency