This paper presents a framework for classification and pose estimation of vehicles in videos by assuming their given 3D models. We rank possible poses and types for each frame and exploit temporal coherence between consecutive frames for refinement. As a novelty, first, we cast the estimation of a vehicle's pose and type as a solution of a continuous optimization problem over space and time. Due to a non-convexity of this problem, good initial starting points are important. We propose to obtain them by a discrete temporal optimization reaching a global optimum on a ranked discrete set of possible types and poses. Second, to guarantee effectiveness of the proposed discrete-continuous optimization, we present a novel way to efficiently reduce the search space of potential 3D model types and poses for each frame for the discrete optimizer. It avoids common expensive evaluation of all possible discretized hypotheses. The key idea towards efficiency lies in a novel combination of detecting the vehicle, rendering the 3D models, matching projected edges to input images, and using a tree structured Markov Random Field to get fast and globally optimal inference and to force the vehicle follow a feasible motion model in the initial phase. Quantitative and qualitative experiments on a variety of videos with vast variation of vehicle types show superior results to state-of-the-art methods.

Abstract. The analysis of human motion is an important task in various surveillance applications. Getting 3D information through a calibrated system might enhance the benefits of such analysis. This paper presents a novel technique to automatically recover both intrinsic and extrinsic parameters for each surveillance camera within a camera network by only using a walking human. The same feature points of a pedestrian are taken to calculate each camera's intrinsic parameters and to determine the relative orientations of multiple cameras within a network as well as the absolute positions within a common coordinate system. Experimental results, showing the accuracy and the practicability, are presented at the end of the paper.

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Nowadays, 2D photography is the common technique for the documentation and digitalization of historical coin inventories. However, by using 2D photography a huge amount of information is lost due to the projection of a 3D structure onto a 2D image. A solution to this problem would be the use of 3D scanning devices to obtain accurate 3D models of the coins. In this paper we show results of scanning 24 historical coins from the Roman and medieval age using a high-accuracy active stereo scanner. We furthermore highlight the various benefits of this acquisition method for coin documentation, coin measurement and coin recognition. The results show that accurate 3D models can be obtained despite the small size and high reflectance of historical coins.

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