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Automatic video analysis from urban surveillance cameras is a fast-emerging field based on computer vision techniques. We present here a comprehensive review of the state-of-the-art computer vision for traffic video with a critical analysis and an outlook to future research directions. This field is of increasing relevance for intelligent transport systems (ITSs). The decreasing hardware cost and, therefore, the increasing deployment of cameras have opened a wide application field for video analytics. Several monitoring objectives such as congestion, traffic rule violation, and vehicle interaction can be targeted using cameras that were typically originally installed for human operators. Systems for the detection and classification of vehicles on highways have successfully been using classical visual surveillance techniques such as background estimation and motion tracking for some time. The urban domain is more challenging with respect to traffic density, lower camera angles that lead to a high degree of occlusion, and the variety of road users. Methods from object categorization and 3-D modeling have inspired more advanced techniques to tackle these challenges. There is no commonly used data set or benchmark challenge, which makes the direct comparison of the proposed algorithms difficult. In addition, evaluation under challenging weather conditions (e.g., rain, fog, and darkness) would be desirable but is rarely performed. Future work should be directed toward robust combined detectors and classifiers for all road users, with a focus on realistic conditions during evaluation.

Systems that automatically assess student programming assignments have been designed and used for over forty years. Systems that objectively test and mark student programming work were developed simultaneously with programming assessment in the computer science curriculum. This article reviews a number of influential automatic assessment systems, including descriptions of the earliest systems, and presents some of the most recent developments. The final sections explore a number of directions automated assessment systems may take, presenting current developments alongside a number of important emerging e-learning specifications.

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This paper proposes and demonstrates a novel method for the detection and classification of individual vehicles and pedestrians in urban scenes. In this scenario, shadows, lights and various occlusions compromise the accuracy of foreground segmentation and hence there are challenges with conventional silhouette-based methods. 2D features derived from histograms of oriented gradients (HOG) have been shown to be effective for detecting pedestrians and other objects. However, the appearance of vehicles varies substantially with the viewing angle and local features may be often occluded. In this paper, a novel method is proposed that overcomes limitations in the use of 2D HOG. Full 3D models are used for the object categories to be detected and the feature patches are defined over these models. A calibrated camera allows an affine transform of the observation into a normalised representation from which '3DHOG' features are defined. A variable set of interest points is used in the detection and classification processes, depending on which points in the 3D model are visible. Experiments on real CCTV data of urban scenes demonstrate the proposed method. The 3DHOG feature is compared with features based on FFT and simple histograms. A baseline method using overlap between wire-frame models and motion silhouettes is also included. The results demonstrate that the proposed method achieves comparable performance. In particular, an advantage of the proposed method is that it is more robust than motion silhouettes which are often compromised in real data by variable lighting, camera quality and occlusions from other objects.