A People-Counting System Using a Hybrid RBF Neural Network.
ABSTRACT A people-counting system using hybrid RBF neural network is described. The proposed system is effective and flexible for the purpose of performing on-line people counting. Compared with other conventional approach, this system introduces a novel method for feature extraction. In this Letter, a new type of hybrid RBF network is developed to enhance the classification performance. The hybrid RBF based people-counting system is thoroughly compared with other approaches. Extensive and promising results were obtained and the analysis indicates that the proposed hybrid RBF based system provides excellent people-counting results in an open passage. A supervised clustering method is proposed for initialising the hybrid RBF network. In order to substantiate the introduction of the hybrid RBF and the proposed supervised clustering algorithm, test results on a vowel recognition benchmark dataset are also included in the Letter.
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- "We consider people counting as an added value to security and safety applications and thus want to avoid top view cameras with limited sensing areas and unfamiliar perspectives for security personnel. When dealing with oblique cameras, one solution to avoid group segmentation is to directly estimate the crowd density by extracting significant features and feed those into a classification framework to obtain an estimation of the number of people as in , . The accuracy of such systems strongly depends on the training set and on the choice of the feature set. "
ABSTRACT: This paper describes a vision based pedestrian detection and tracking system which is able to count people in very crowded situations like escalator entrances in underground stations. The proposed system uses motion to compute regions of interest and prediction of movements, extracts shape information from the video frames to detect individuals, and applies texture features to recognize people. A search strategy creates trajectories and new pedestrian hypotheses and then filters and combines those into accurate counting events. We show that counting accuracies up to 98 % can be achieved.Video and Signal Based Surveillance, 2006. AVSS '06. IEEE International Conference on; 12/2006
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ABSTRACT: To solve the problem that RBF neural networks has a weakness in generality, a new structure of RBF neural network called hybrid RBF neural network is studied in this article. Comparing to general RBF networks, the proposed RBF network has an advantage in achieve better classification performance though partition the input domain flexibly and effectively into the hidden-layer. The number of hidden neurons and the network weight values are automatically determined on the basis of fuzzy C-means algorithm and PSO algorithm under the supervision of the network performance. This learning proposal is applied and testified its advantage in the soft sensor modeling of temperature measurement of Texaco gasifier .Information Acquisition, 2006 IEEE International Conference on; 09/2006
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ABSTRACT: The study of small target recognition in low SNR (Signal Noise Ratio) is the key problem about processing of forward-looking infrared (FLIR) images information. Eight features of objects based on IR radiation characteristics and wavelet-based are presented. These features are used to a radial basis function (RBF) network as input for learning and classification. The propose recognition algorithm is invariant to the translation, rotation, and scale channel of a shape. Experiments by real infrared images and noisy images are performed, and recognition results show that the method is very effective.Advances in Neural Networks - ISNN 2007, 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part II; 01/2007