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How is the sliding window algorithm understood using convolutional algorithm?
In this article, I will discuss how the sliding window algorithm can be understood using the convolutional algorithm. But before that, let us build the intuition for it. Object detection includes the object classification and the object localization concepts in great detail.
Which is faster R-CNN or RPN algorithm?
Faster R-CNN is an object detection algorithm published by Microsoft in 2015.It is the first succee of the end-to-end implementation in Deep Learning. The workflow is as follows.
How is sliding window detection used in object detection?
In object detection problems, we generally have to find all the possible objects in the image like all the cars in the image, all the pedestrians in the image, all the bikes in the image, etc. To achieve this, we use an algorithm known as Sliding window detection. Let us understand this algorithm.
How is the IOU used in the sliding window algorithm?
As per the IoU, we can choose the most accurate bounding box among the other bounding boxes for the same object using IoU algorithm. More generally, IoU is a measure of the overlap between two bounding boxes. It is defined as the ratio of the size of the intersection between the bounding boxes to the union between the boxes.
How do you use the sliding window technique?
Applying sliding window technique : We compute the sum of first k elements out of n terms using a linear loop and store the sum in variable window_sum. Then we will graze linearly over the array till it reaches the end and simultaneously keep track of maximum sum.
How to calculate maximum sum in window sliding?
This method requires nested for loop, the outer for loop starts with the starting element of the block of k elements and the inner or the nested loop will add up till the k-th element. Consider the below implementation : // Returns maximum sum in a subarray of size k. // Consider all blocks starting with i.