New feature-preserving filter algorithm based on a priori knowledge of pixel types

The concept and algorithmic details of a new corrupted-pixel-identification- (CPI)-based estimation filter are presented. The approach is by transforming a noisy subimage centered on a corrupted pixel into its discrete cosine transform (DCT) domain, and approximating the transformed subimage by its DC (average) coefficient only, an estimation of the noise distribution is made by combining the knowledge of the number of corrupted pixels in the subimage and the pixel intensity of the noise term. This enables the DC coefficient of the restored image in the DCT domain to be determined, and from this, the restored pixel intensity can be calculated by an inverse DCT. The whole restored image can be obtained after all the corrupted pixels are exhausted. From an extensive performance evaluation, it was found that the new algorithm has a number of desirable characteristics. First, the CPI-based estimation algorithm performs extremely well when heavily degraded images are concerned. Second, the CPI-based estimation algorithm has acceptable feature-preserving properties, far better than the conventional median filter. Third, the new algorithm can be applied iteratively to the same noisy image. Fourth, the computing speed of the CPI-based estimation algorithm is almost three times faster than the conventional median filter, and 1.6 times faster than the original CPI algorithm, making it the fastest algorithm in this class so far. ©1996 Society of Photo – Optical Instrumentation Engineers.published_or_final_versio

A self-growing Bayesian network classifier for online learning of human motion patterns

This paper proposes a new self-growing Bayesian network classifier for online learning of human motion patterns (HMPs) in dynamically changing environments. The proposed classifier is designed to represent HMP classes based on a set of historical trajectories labeled by unsupervised clustering. It then assigns HMP class labels to current trajectories. Parameters of the proposed classifier are recalculated based on the augmented dataset of labeled trajectories and all HMP classes are accordingly updated. As such, the proposed classifier allows current trajectories to form new HMP classes when they are sufficiently different from existing HMP classes. The performance of the proposed classifier is evaluated by a set of real-world data. The results show that the proposed classifier effectively learns new HMP classes from current trajectories in an online manner. © 2010 IEEE.published_or_final_versionThe 2010 International Conference of Soft Computing and Pattern Recognition (SoCPaR 2010), Paris, France, 7-10 December 2010. In Proceedings of SoCPaR2010, 2010, p. 182-18

Vehicle-Type Identification Through Automated Virtual Loop Assignment and Block-Based Direction-Biased Motion Estimation

This paper presents a method of automated virtual loop assignment and direction-based motion estimation. The unique features of our approach are that first, a number of loops are automatically assigned to each lane. The merit of doing this is that it accommodates pan-tilt-zoom (PTZ) actions without needing further human interaction. Second, the size of the virtual loops is much smaller for estimation accuracy. This enables the use of standard block-based motion estimation techniques that are well developed for video coding. Third, the number of virtual loops per lane is large. The motion content of each block may be weighted and the collective result offers a more reliable and robust approach in motion estimation. Comparing this with traditional inductive loop detectors (ILDs), there are a number of advantages. First, the size and number of virtual loops may be varied to fine-tune detection accuracy. Second, it may also be varied for an effective utilization of the computing resources. Third, there is no failure rate associated with the virtual loops or physical installation. As the loops are defined on the image sequence, changing the detection configuration or redeploying the loops to other locations on the same image sequence requires only a change of the assignment parameters. Fourth, virtual loops may be reallocated anywhere on the frame, giving flexibility in detecting different parameters. Our simulation results indicate that the proposed method is effective in type classification.published_or_final_versio

Fast and parallel video encoding by workload balancing

The issue of balancing the macroblocks (MB) computing workload across the processors are explored. These includes, the prediction of the workload based on the previous frame workload and the scheduling of the MB bounded by the locality constraint. The algorithm was implemented on an IBM SP2, and the results showed that the reduction in the worst case delay is around 19-23%, with both the prediction and scheduling overhead taken into account. Because of the critical path reduction, the overall processor utilization was increased and the overall coding rate improved.published_or_final_versio

Generalized parallelization methodology for video coding

This paper describes a generalized parallelization methodology for mapping video coding algorithms onto a multiprocessing architecture, through systematic task decomposition, scheduling and performance analysis. It exploits data parallelism inherent in the coding process and performs task scheduling base on task data size and access locality with the aim to hide as much communication overhead as possible. Utilizing Petri-nets and task graphs for representation and analysis, the method enables parallel video frame capturing, buffering and encoding without extra communication overhead. The theoretical speedup analysis indicates that this method offers excellent communication hiding, resulting in system efficiency well above 90%. A H.261 video encoder has been implemented on a TMS320C80 system using this method, and its performance was measured. The theoretical and measured performances are similar in that the measured speedup of the H.261 is 3.67 and 3.76 on four PP for QCIF and 352×240 video, respectively. They correspond to frame rates of 30.7 frame per second (fps) and 9.25 fps, and system efficiency of 91.8% and 94% respectively. As it is, this method is particularly efficient for platforms with small number of parallel processors.published_or_final_versio

An intelligent navigator for mobile vehicles

This paper presents an intelligent navigation method for navigation of a mobile vehicle in unknown environments. The proposed navigator consists of three modules: Obstacle Avoidor, Environment Evaluator and Navigation Supervisor. The Obstacle Avoidor is a fuzzy controller whose rule base is learnt through reinforcement learning. A new and powerful training method is proposed to construct the fuzzy rules automatically. The Navigation Supervisor determines the tactical requirement of avoiding obstacles or moving towards the goal location at each action step so that the vehicle can achieve its task without colliding with obstacles. The effectiveness of the learning method and the whole navigator are verified by simulation.published_or_final_versio

A multiple-goal reinforcement learning method for complex vehicle overtaking maneuvers

In this paper, we present a learning method to solve the vehicle overtaking problem, which demands a multitude of abilities from the agent to tackle multiple criteria. To handle this problem, we propose to adopt a multiple-goal reinforcement learning (MGRL) framework as the basis of our solution. By considering seven different goals, either Q-learning (QL) or double-action QL is employed to determine action decisions based on whether the other vehicles interact with the agent for that particular goal. Furthermore, a fusion function is proposed according to the importance of each goal before arriving to an overall but consistent action decision. This offers a powerful approach for dealing with demanding situations such as overtaking, particularly when a number of other vehicles are within the proximity of the agent and are traveling at different and varying speeds. A large number of overtaking cases have been simulated to demonstrate its effectiveness. From the results, it can be concluded that the proposed method is capable of the following: 1) making correct action decisions for overtaking; 2) avoiding collisions with other vehicles; 3) reaching the target at reasonable time; 4) keeping almost steady speed; and 5) maintaining almost steady heading angle. In addition, it should also be noted that the proposed method performs lane keeping well when not overtaking and lane changing effectively when overtaking is in progress. © 2006 IEEE.published_or_final_versio

Crowd counting and segmentation in visual surveillance

Reference no. MP-PD.8In this paper, the crowd counting and segmentation problem is formulated as a maximum a posterior problem, in which 3D human shape models are designed and matched with image evidence provided by foreground/background separation and probability of boundary. The solution is obtained by considering only the human candidates that are possible to be un-occluded in each iteration, and then applying on them a validation and rejection strategy based on minimum description length. The merit of the proposed optimization procedure is that its computational cost is much smaller than that of the global optimization methods while its performance is comparable to them. The approach is shown to be robust with respect to severe partial occlusions. ©2009 IEEE.published_or_final_versionThe 16th IEEE International Conference on Image Processing (ICIP 2009), Cairo, Egypt, 7-10 November 2009. In International Conference on Image Processing Proceedings, 2009, p. 2573-257

Arm pose modeling for visual surveillance

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A Novel And Fast Feature Based Motion estimation ALgorithm Through extraction Of Background And Object

This paper presents a novel and fast Feature Based Motion Estimation algorithm which is developed for typical video-phone scenario. In essence it combines the technique of object extraction with traditional block based motion estimation methods by estimating the background and extracting the moving object continuously in the first stage, then performs a block based motion estimation on the extracted. Simulation of the algorithm with full search as core shows that the estimation time can be reduced by as much as 50%, while the MSE and PSNR remain almost the same as the full search results.published_or_final_versio