30762676
Kernel particle filter: iterative sampling for efficient visual tracking
Particle filter has recently received attention in computer vision applications due to attributes such as its ability to carry multiple hypotheses and its relaxation of the linearity assumption. Its shortcoming is increase in complexity with state dimension. We present kernel particle filter as a variation of particle filter with improved sampling efficiency and performance in visual tracking. Unlike existing methods that use stochastic or deterministic optimization procedures to find the modes in a likelihood function, we redistribute particles by invoking kernel-based representation of densities and introducing mean shift as an iterative mode-seeking procedure, in which particles move towards dominant modes while still maintaining as fair samples from the posterior. Experiments on face and limb tracking show that the algorithm is superior to conventional particle filter in handling weak dynamic models and occlusions with 60% fewer particles in 3-9 dimensional spaces.
61362315
Cyclic articulated human motion tracking by sequential ancestral simulation
Accurate tracking of cyclic human motion in video data helps in developing computer-aided applications such as gait analysis, visual surveillance, patient rehabilitation, etc. This paper presents a novel technique for tracking cyclic human motion based on decomposing complex cyclic motion into components and maintaining coupling between components. The decomposition reduces the dimensionality of the problem and enables a graphical modeling of the articulated human body. The coupling between components is modeled by their phase relationship and represented as directed edges in Bayesian networks and undirected edges in Markov random fields. Such coupling is maintained in tracking through ancestral simulation (AS) and Markov potentials in a sequential Monte Carlo tracking framework. We show that the approach handles severe self-occlusion and foreign body occlusion with improved accuracy and efficiency.
782406
Data Compression
5449727
Robust tracking of cyclic nonrigid motion
Cyclic motion underlies several human activities including exercising, running, and walking. Accurate tracking of such motion in video data helps in developing computer-aided applications such as gait analysis, person identification, patient rehabilitation, etc. This paper presents a set of novel techniques for tracking cyclic human motion based on decomposing complex cyclic motion into simpler motion components and introducing phase coupling between the components. The intensity of coupling is adaptively adjusted during tracking such that a strong coupling is triggered when self-occlusion occurs. In our experiments we use sequential Monte Carlo methods for tracking a walking human. We show that this adaptive phase coupling of component motions handles occlusion and self-occlusion with significantly improved accuracy while avoiding the limitations caused by a poorly trained dynamic model.
417369
Multiple object tracking with kernel particle filter
A new particle filter, kernel particle filter (KPF), is proposed for visual tracking for multiple objects in image sequences. The KPF invokes kernels to form a continuous estimate of the posterior density function and allocates particles based on the gradient derived from the kernel density estimate. A data association technique is also proposed to resolve the motion correspondence ambiguities that arise when multiple objects are present. The data association technique introduces minimal amount of computation by making use of the intermediate results obtained in particle allocation. We show that KPF performs robust multiple object tracking with improved sampling efficiency.
6876954
A New Backoff Algorithm for IEEE 802.11 Distributed Coordination Function
DCF (Distributed Coordination Function) of IEEE802.11 MAC protocols uses binary exponential increasing mechanism, called BEB (Binary Exponential Backoff) algorithm. Due to the lack of a mechanism evaluating the channel condition, it can not tune the value of contention windows adaptively, according to the actual network traffic. By introducing the network allocation vector count in routing protocol to approximate the intensity of surrounding traffic of nodes, the algorithm has been improved, so that the contention windows of backoff mechanism are adjusted reasonably according to the traffic of WLAN. Simulation experiments are done and the results prove that the improved algorithm provides a better channel utilization and reduces the average transmission delay.
10631202
Real-time scheduling in a programmable radar signal processor
The new trend of designing radar digital signal processors is not only to improve their processing speed by adopting the parallel multi-processor architecture but also to make their infrastructure flexible so that one generic digital signal processor framework can be applied to a variety of different radar systems. The key technology to make such radar digital signal processor architecture programmable, or flexible, is the scheduler which manages resources of digital signal processing in real-time according to system requirements. In this paper, the architecture of the programmable radar signal processor (PRSP) is presented, and a real-time scheduling algorithm used in PRSP is thoroughly discussed.
6881358
Probabilistic real-time guarantees for component-oriented phased array radars
In recent years, many modern phased array radars are built with commercial-off-the-shelf components, and the functions of many hardware components are also re-implemented by software modules. In such systems, radar tasks could be modelled as distributed real-time tasks, which require end-to-end deadline guarantees and have precedence constraints. Different from most previous work on either algorithms with restrictions in resource utilization or heuristics without analytical ways for schedulability guarantees, the objective of This work is to propose a joint real-time scheduling algorithm for both transmitter/receiver and signal processor workloads with an analytical framework for off-line probabilistic analysis and online admission control. The strength of our approach is verified by analysis results and a series of experiments based on a real phased array radar for air defense frigates [A. G. Huizing et al. (1996)].
9272685
Genetic algorithm for affine point pattern matching
Point pattern matching (PPM) is an important topic in the fields of computer vision and pattern recognition. According to if there exists a one to one mapping between the two point sets to be matched, PPM can be divided into the case of complete matching and the case of incomplete matching. According to if utilizing information other than 2-D image coordinates, PPM can be divided into labelled point-matching case and unlabelled point-matching case. Using partial Hausdorff distance, this paper presents a genetic algorithm (GA) based method to solve the incomplete unlabelled matching problem under general affine transformation. Since it successfully reduces the solution space of GA by constructing 'feature ellipses' of point sets, the method can achieve high computing efficiency and good matching results. Theoretical analysis and simulation results show that the new algorithm is very effective.
12856888
On the rate distortion function of Bernoulli Gaussian sequences
We study the rate distortion function of the Bernoulli-Gaussian random variable which can be used to model sparse signals. Both lower and upper bounds on the rate distortion function are given.We show that the bounds are almost tight in the low distortion regime for sparse signals. Interestingly, a naive coding scheme is near-optimal in this scenario.