14796999
Robust color-based tracking
Color as a distinct feature is widely used for object representation and tracking. However, color-based tracking is often influenced by clutter background and illumination variation. This paper presents a robust color-based tracking method, in which robust color feature is extracted for constructing the observation model under the modified particle filter tracking framework. The object is represented by its dominant color, and the weighted histogram with spatial information of the dominant color is used to optimize object models. In the particle filter framework, an extended iterated likelihood weighting scheme is employed to utilize more valuable particles. The experimental results show it is a real-time robust tracker, and it can obtain more than 30 fps with 2.4 G CPU and 512 MRAM.
34743400
An optimal weather condition dependent approach for emission planning in urban areas
Abstract The urban air quality strongly depends on the weather conditions. When unfavorable weather conditions are forecasted, it is desirable to reduce a certain amount of pollutant emissions to maintain a given level of air quality. In this paper, we propose and demonstrate a new approach to find the optimal reduction, and we contribute to the methodology of decision support of short-term emission control. This approach is based on weather forecasts and state-of-the-art 3-dimensional numerical air-quality prediction models by solving an optimal control problem with the emission cuts as the control variables. The objective of the optimal problem is to minimize the total cost due to the emission cuts, subject to feasibility constraints, system-governing model constraints, and target constraints. When a high-resolution numerical model is used as the state constraint, the problem can become a very high dimensional one. A practical approach to solving this problem with high dimension is proposed, based on the adjoint technique. The proposed approach is demonstrated with two computational test cases in Jinan, China for the control of sulfur dioxide. The results show the capability and computational efficiency of the method and suggest a promising potential for emission planning applications based on weather forecasts.
6728099
A Novel Generic Hebbian Ordering-Based Fuzzy Rule Base Reduction Approach to Mamdani Neuro-Fuzzy System
There are two important issues in neuro-fuzzy modeling: (1) interpretabilitythe ability to describe the behavior of the system in an interpretable wayand (2) accuracythe ability to approximate the outcome of the system accurately. As these two objectives usually exert contradictory requirements on the neuro-fuzzy model, certain compromise has to be undertaken. This letter proposes a novel rule reduction algorithm, namely, Hebb rule reduction, and an iterative tuning process to balance interpretability and accuracy. The Hebb rule reduction algorithm uses Hebbian ordering, which represents the degree of coverage of the samples by the rule, as an importance measure of each rule to merge the membership functions and hence reduces the number of the rules. Similar membership functions (MFs) are merged by a specified similarity measure in an order of Hebbian importance, and the resultant equivalent rules are deleted from the rule base. The rule with a higher Hebbian importance will be retained among a set of rules. The MFs are tuned through the least mean square (LMS) algorithm to reduce the modeling error. The tuning of the MFs and the reduction of the rules proceed iteratively to achieve a balance between interpretability and accuracy. Three published data sets by Nakanishi (Nakanishi, Turksen, & Sugeno, 1993), the Pat synthetic data set (Pal, Mitra, & Mitra, 2003), and the traffic flow density prediction data set are used as benchmarks to demonstrate the effectiveness of the proposed method. Good interpretability, as well as high modeling accuracy, are derivable simultaneously and are suitably benchmarked against other well-established neuro-fuzzy models.
1617013
Motion Path Synthesis for Intelligent Avatar
In this paper, we present a new motion generation technique, called motion path synthesis. The objective is to generate a motion for an intelligent avatar to move along a planned route using a pre-existing motion library. First, motion primitives, each defined as a dynamic motion segment, are extracted from the motion library. Then a motion graph is constructed based on the motion primitives and their connectivities. Within this motion graph, the desired realistic motion for avatars can be synthesized through a two-stage process: search an optimal motion path within the motion graph, joint the motion path and adapt it to the route. The experiment shows the effectiveness of the presented technique.
14281509
A Quick Group Search Optimizer with Passive Congregation and its Convergence Analysis
This paper presents a quick group search optimizer with passive congregation (QGSOPC) for optimum design of pin connected structure. The algorithm is based on the quick group search optimizer (QGSO). The QGSOPC algorithm is verified and compared with the HPSO, GSO and QGSO algorithm used for the designs of two planar and spatial truss structures. The results show that the QGSOPC algorithm not only has preferable convergence rate and accuracy, but also it has the best stability of convergence rate and accuracy. It is desired for QGSOPC to be used more effectively for practical structural optimal design problems.
37555026
Incomplete motion feature tracking algorithm in video sequences
To effectively track incomplete motion features, a novel feature tracking algorithm for motion capture is presented. According to feature attributes and relationship among features, extracted features are classified as four types of features. Then different strategies are applied to track different kinds of features. To verify the tracks, cross correlation test and predicted 3D model based test are used to test and remove outliers. Experimental results demonstrate the effectiveness of our algorithm.
15250571
Frequent Itemset Mining Based on Heuristic Two Level Counting
Recently, many enchanced Apriori algorithms have been proposed to efficiently generate all frequent itemsets from datasets in data mining field. Although efficient techniques were presented, those algorithms are either time-consuming or memory-consuming. To address the issue further, a new algorithm, which introduced a novel support counting method, heuristic two level counting, is proposed. HTLC method adopts an improved itemset generating technology in the generation process of low level itemsets, which promotes the production of low level frequent itemsets or candidate itemsets. It also applies a heuristic traversal technology which speeds up one pass over datasets and support counting technology which largely reduces the number of passes over datasets to the generation of high level frequent itemsets. Finally, the experimental results show that it outperforms existing Apriori-like algorithms in mostly datasets