Gesture segmentation based on YCrCb ellipse skin model and background subtraction

Tan, Xianghua; Jin, Yueqiang; Feng, Guizhen; Xiao, Jiang

doi:10.1109/pic.2015.7489862

Cited by 3 publications

(1 citation statement)

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“…Local and global features of a vehicle will be extracted from the luminance and chromatic components of color images of the vehicle. The components are YCrCb that is efficient for a recognition task [21]. Y is the luminance component that has a grayscale color space, and it represents brightness.…”

Section: Overview Of the Proposed Techniquementioning

confidence: 99%

Vehicle Type Classification Using an Enhanced Sparse-Filtered Convolutional Neural Network With Layer-Skipping Strategy

2020

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In this paper, a vehicle type classification approach is proposed by using an enhanced feature extraction technique based on Sparse-Filtered Convolutional Neural Network with Layer-Skipping strategy (SF-CNNLS). To extract rich and discriminant vehicle features, we introduce Three-Channels of SF-CNNLS (TC-SF-CNNLS) as the feature extraction technique. Local and global features of vehicles are extracted from three channels of an image which are, luminance and chromatic components. This technique is inspired by how human eyes differentiating objects that share almost similar features. TC-SF-CNNLS is tested with a benchmark dataset that provides frontal-view images to classify vehicle types of the bus, passenger car, taxi, minivan, SUV, and truck with Softmax Regression as a classifier. This test aims to observe the ability of this technique in differentiating vehicles with almost similar features but different classes. A test is also conducted with the self-obtained dataset (SPINT) to observe the effectiveness of this technique. The results are observed based on accuracy, precision, recall, and f-score, whereby, TCSF-NNLS has successfully recognized all the classes with an average accuracy of 0.905, precision is between 0.8629 to 0.9548, recall is between 0.83 to 0.96 and f-score is between 0.8564 to 0.9523. In addition, this technique is able to outperform other existing techniques with an average accuracy of 93.% compared to only 89.2% when 5 classes of vehicles are tested. INDEX TERMS Vehicle type recognition, convolutions neural network, deep learning, computational intelligence.

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Section: Overview Of the Proposed Techniquementioning

confidence: 99%