Aim: This paper proposes novel object detection (OD) approach based on a thorough examination of the image's details and its approximate density chart.
Results: Our proposed OD approach is divided into two phases. Knowledge about Spatial Distribution of Objects obtained from a density map that is used to compute initial object positions. With the aid of the original object positions estimated, a saliency map that provides entity boundaries is then used to calculate the bounding boxes with precision, which is inspired by human attention to detail. The scale variance of objects induced by uncertain perspective is a common problem in object density map estimation. A new method for estimating the prior focus for map for any image is proposed. Sobel and Roberts Edge Detection Algorithm are used in this study. The proposed approach is based on sparse defocus dictionary learning on a newly constructed dataset. The focus power is determined by the number of non-zero coefficients of the dictionary atoms.
Conclusion: The algorithm's output can capture spatial features and pick the threshold type in a variety of ways.
HIGHLIGHTS:
Object detection based on spectral analysis using Sobel and Roberts edge detection algorithm proved to be effective when compared with existing methodologies
