To do this, set the MergeDetections property to false. For more flexibility and control of merging parameters, you can use the function in place of the MergeDetections algorithm. Set this property to false to output the unmerged bounding boxes. Set this property to true to merge bounding boxes using a mean-shift based algorithm. This property controls whether similar detections are merged. Merge detection control, specified as true or false. Increasing the window stride beyond can lead to a greater number of missed detections. However, doing so increases computation time. When you specify this value as a scalar, the stride is the same for both x and y.ĭecreasing the window stride can increase the detection accuracy. When you specify this value as a vector, the first and second elements are the stride size in the x and y directions. The detector uses the window stride to slide the detection window across the image. Detection window stride in pixels, specified as a scalar or a two-element vector. Vary the threshold over a range of values to find the optimum value for your data set. The higher the threshold value, the more stringent the requirements are for the classification. You can increase this value when there are many false detections. The threshold controls whether a subregion gets classified as a person. Use this threshold to control the classification of individual image subregions during multiscale detection. People classification threshold, specified as a nonnegative scalar value. Download Cornell Activity Datasets and Code. Being able to detect and recognize human activities is essential for several applications, including smart homes and personal assistive robotics. Human Activity Detection from RGBD Images. Personal Robotics, Co-Robots, Robotic Perception. C/C++ Code Generation Generate C and C++ code using MATLAB. “Histograms of Oriented Gradients for Human Detection,”Proceedings of IEEE Conference on. Learn more about human-body-detection Computer Vision System Toolbox.
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