Real-time detection and tracking of multiple objects with partial decoding in H.264/AVC bitstream domain

In this paper, we show that we can apply probabilistic spatiotemporal macroblock filtering (PSMF) and partial decoding processes to effectively detect and track multiple objects in real time in H.264|AVC bitstreams with stationary background. Our contribution is that our method cannot only show fast processing time but also handle multiple moving objects that are articulated, changing in size or internally have monotonous color, even though they contain a chaotic set of non-homogeneous motion vectors inside. In addition, our partial decoding process for H.264|AVC bitstreams enables to improve the accuracy of object trajectories and overcome long occlusion by using extracted color information.Comment: SPIE Real-Time Image and Video Processing Conference 200

Reconstructing the Traffic State by Fusion of Heterogeneous Data

We present an advanced interpolation method for estimating smooth spatiotemporal profiles for local highway traffic variables such as flow, speed and density. The method is based on stationary detector data as typically collected by traffic control centres, and may be augmented by floating car data or other traffic information. The resulting profiles display transitions between free and congested traffic in great detail, as well as fine structures such as stop-and-go waves. We establish the accuracy and robustness of the method and demonstrate three potential applications: 1. compensation for gaps in data caused by detector failure; 2. separation of noise from dynamic traffic information; and 3. the fusion of floating car data with stationary detector data.Comment: For more information see http://www.mtreiber.de or http://www.akesting.d

Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction

We propose a new neurally-inspired model that can learn to encode the global relationship context of visual events across time and space and to use the contextual information to modulate the analysis by synthesis process in a predictive coding framework. The model learns latent contextual representations by maximizing the predictability of visual events based on local and global contextual information through both top-down and bottom-up processes. In contrast to standard predictive coding models, the prediction error in this model is used to update the contextual representation but does not alter the feedforward input for the next layer, and is thus more consistent with neurophysiological observations. We establish the computational feasibility of this model by demonstrating its ability in several aspects. We show that our model can outperform state-of-art performances of gated Boltzmann machines (GBM) in estimation of contextual information. Our model can also interpolate missing events or predict future events in image sequences while simultaneously estimating contextual information. We show it achieves state-of-art performances in terms of prediction accuracy in a variety of tasks and possesses the ability to interpolate missing frames, a function that is lacking in GBM

Ensemble representation of uncertainty in Lagrangian satellite rainfall estimates

A new algorithm called Lagrangian Simulation (LSIM) has been developed that enables the interpolation uncertainty present in Lagrangian satellite rainfall algorithms such as the Climate Prediction Center (CPC) morphing technique (CMORPH) to be characterized using an ensemble product. The new algorithm generates ensemble sequences of rainfall fields conditioned on multiplatform multisensor microwave satellite data, demonstrating a conditional simulation approach that overcomes the problem of discontinuous uncertainty fields inherent in this type of product. Each ensemble member is consistent with the information present in the satellite data, while variation between members is indicative of uncertainty in the rainfall retrievals. LSIM is based on the combination of a Markov weather generator, conditioned on both previous and subsequent microwave measurements, and a global optimization procedure that uses simulated annealing to constrain the generated rainfall fields to display appropriate spatial structures. The new algorithm has been validated over a region of the continental United States and has been shown to provide reliable estimates of both point uncertainty distributions and wider spatiotemporal structures