Highly efficient neoteric histogram-entropy-based rapid and automatic thresholding method for moving vehicles and pedestrians detection

Thresholding for segmentation is an important key step and necessary process in various applications. Estimating an accurate threshold value for a complex and coarse image is computationally expensive and lacks accuracy and stability. This study is aimed at developing a general histogram-entropy-based thresholding method, referred as our HEBT method, for fast and efficient automatic threshold value evaluation. In the proposed method, the probability density function and Shannon entropy derived from 1D bimodal histogram have been used to find the optimal threshold values automatically. The proposed method implemented with a three-frame differencing segmentation technique has been tested on real-time datasets - change detection 2012, change detection 2014, and Wallflower - to identify pedestrians and vehicles. The performance of our HEBT method has been compared with six state-of-the-art automatic thresholding methods. The experimental segmented image results confirmed that our HEBT method is more adaptable and better suited for real-time systems with severe challenging conditions of great variations. Further, the new HEBT method achieved the best segmentation results with highest values of several performance parameters, i.e. recall, precision, similarity, and f-measure. Interestingly, the computation time is the lowest for the proposed method than the state-of-the-art methods, promising its application for a fast and effective image segmentation.