Abstract
This paper presents a new method and data analysis for reducing false alarm of vehicle targets in real-time classification [1] by introducing two new parameters, i.e., average confidence and decision parameter. The main challenge is to do tracking and classification by using infrared sensor. The greater the number of data set provided, greater is the accuracy of classification. The confidence factor in this algorithm is defined as the percentage of target occurrences in the past 25 frames and varies linearly with number of detections. An optimization of the confidence factor could be an area of further work in the proposed algorithm.
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Singh, A., Kumar, R., Tripathi, R.P. (2019). Study and Analysis of Different Kinds of Data Samples of Vehicles for Classification by Bag of Feature Technique. In: Ray, K., Sharma, T., Rawat, S., Saini, R., Bandyopadhyay, A. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 742. Springer, Singapore. https://doi.org/10.1007/978-981-13-0589-4_6
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DOI: https://doi.org/10.1007/978-981-13-0589-4_6
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