KEYWORDS: General packet radio service, Motion models, Feature extraction, Visual process modeling, Process modeling, Data modeling, Computer programming, Lithium, Visualization, Machine vision
Pedestrian path forecasting is one of the recently emerging applications in visual crowd analysis and modeling. Moreover, of the attempts proposed to date, only a few have considered that the undergoing interaction among agents is a key factor in determining their walking trends in a given scene. To this end, we propose a simple yet efficient framework for pedestrian path prediction in crowded scenes. First, we extract motion features related to the target pedestrian and its nearby neighbors. Second, we adopt an autoencoder feature-learning model to further enhance the representation of the extracted features. Finally, we utilize a Gaussian process regression model to infer the potential future trajectories of the target pedestrians given their walking history in the scene. We performed experiments using a challenging dataset, and our method yielded promising results and outperformed traditional methods proposed in the literature.
A real-time video denoising algorithm based on adaptive multi-layers background is presented in this paper. The
adaptive multi-layers background, which is aimed at the demand for video denoising, not only applies to static scene,
but also applies to unstable scene. The modification of multi-layers background would be accomplished by a short-term
adjustment after the scene changed. Based on the multi-layers background, the video denoising algorithm promotes the
distinct vision of static scene and the short-term inactive region. The model of multi-layers background adjusts step by
step adaptively. So it is not in need of a long-term delay and expensive computation. Experiments demonstrate the
effectiveness of this algorithm.
This paper describes the complete procedure of framework design on a video coding system for error-prone heterogeneous network environment. First of all, system requirements are analyzed and the targets of system design are emphasized on three categories: 1. improve video performance under the constraints of network bandwidth and computational complexity; 2. provide error robust mechanism when packet loss occur; 3. add capability of layered video coding for heterogenous network transmission. Then, ITU-T H.263+ recommendation is introduced, especially about its 16 optional enhanced coding modes: feature, effect and benefit. System design on mode selection is somewhat a trade-off, one hand is improvement on subjective quality of video or other system targets, the other hand is influence on time delay and complexity (computational load, data dependency, ease of implementation, etc.). Five enhanced coding modes are preferred in strong reason for our purpose: Advanced INTRA Coding mode, Deblocking Filter mode, Modified Quantization mode, Slice Structured mode and Temporal, SNR and Spatial Scalability mode. At last, some useful and important system elements beyond H.263+ recommendation are discussed: RTP packetization, error concealment and error tracking. Application shows that all the targets are easily reached by the implementation based on this design.
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