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Many computer vision applications such as augmented reality require head pose estimation. As far as the real-time implementation of head pose estimation on relatively resource limited mobile platforms is concerned, it is required to satisfy real-time constraints while maintaining reasonable head pose estimation accuracy. The introduced head pose estimation approach in this paper is an attempt to meet this objective. The approach consists of the following components: Viola-Jones face detection, color-based face tracking using an online calibration procedure, and head pose estimation using Hu moment features and Fisher linear discriminant. Experimental results running on an actual mobile device are reported exhibiting both the real- time and accuracy aspects of the developed approach.

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In image and video analysis, distance maps are frequently used. They provide the (Euclidean) distance (ED) of background pixels to the nearest object pixel. In a naive implementation, each object pixel feeds its (exact) ED to each background pixel; then the minimum of these values denotes the ED to the closest object. Recently, the Fast Exact Euclidean Distance (FEED) transformation was launched, which was up to 2x faster than the fastest algorithms available. In this paper, first additional improvements to the original FEED algorithm are discussed. Next, a timed version of FEED (tFEED) is presented, which generates distance maps for video sequences by merging partial maps. For each object in a video, a partial map can be calculated for different frames, where the partial map for fixed objects is only calculated once. In a newly developed, dynamic test-environment for robot navigation purposes, tFEED proved to be up to 7x faster than using FEED on each frame separately. It is up to 4x faster than the fastest ED algorithm available for video sequences and even 40% faster than generating city-block or chamfer distance maps for frames. Hence, tFEED is the first real time algorithm for generating exact ED maps of video sequences.

The MPEG-4 video standard extends the traditional frame-based processing with the option to compose several video objects (VO) superimposed on a background sprite image. In our previous work, we presented a distributed, multiprocessor based, scalable implementation of an MPEG-4 arbitrary-shaped decoder, which forms together with the background sprite decoder an essential part for further scene rendering. For control of the multiprocessor architecture, we have constructed a Quality-of-Service (QoS) management that monitors the availability of required data and distributes the processing of individual tasks with guaranteed or best-effort services of the platform. However, the proposed architecture with the combined guaranteed and best-effort services poses problems for real-time scene rendering. In this paper, we present a technique for proper run-time rendering of the final scene after decoding one VO Layer. The individual video-object monitors check the data availability and select the highest quality for the final scene rendering. The algorithm operates hierarchically both at the scene level and at the task level of the video object processing. Whereas the earlier work on scalable implementation concentrated only on guaranteed services, we now introduce a new element in the system architecture for the real-time control and fall back mechanism of the best-effort services. This element is based on first, controlling data availability at task level, and second, introducing the propagation service to QoS management. We present our simulation results in the comparison with the standard "frame-skipping" technique that is the only currently available solution to this type of rendering a scalable processing.

This paper presents a software framework providing a platform for parallel and distributed processing of video data on a cluster of SMP computers. Existing video-processing algorithms can be easily integrated into the framework by considering them as atomic processing tiles (PTs). PTs can be connected to form processing graphs that model the data flow of a specific application. This graph also defines the data dependencies that determine which tasks can be computed in parallel. Scheduling of the tasks in this graph is carried out automatically using a pool-of-tasks scheme. The data format that can be processed by the framework is not restricted to image data, such that also intermediate data, like detected feature points or object positions, can be transferred between PTs. Furthermore, the processing can optionally be carried out efficiently on special-purpose processors with separate memory, since the framework minimizes the transfer of data. Finally, we describe an example application for a multi-camera view-interpolation system that we successfully implemented on the proposed framework.