![]() ![]() Since these are the first initial set of bounding boxes presented to our algorithm we will assign them unique IDs. Figure 1 above demonstrates accepting a set of bounding box coordinates and computing the centroid. Once we have the bounding box coordinates we must compute the “centroid”, or more simply, the center (x, y)-coordinates of the bounding box. These bounding boxes can be produced by any type of object detector you would like (color thresholding + contour extraction, Haar cascades, HOG + Linear SVM, SSDs, Faster R-CNNs, etc.), provided that they are computed for every frame in the video. The centroid tracking algorithm assumes that we are passing in a set of bounding box (x, y)-coordinates for each detected object in every single frame. Update July 2021: Added section on alternative object trackers, including object trackers built directly into the OpenCV library.įigure 1: To build a simple object tracking algorithm using centroid tracking, the first step is to accept bounding box coordinates from an object detector and use them to compute centroids.To learn how to get started building your first object tracking with OpenCV, just keep reading! In future posts in this object tracking series, I’ll start going into more advanced kernel-based and correlation-based tracking algorithms. In today’s blog post, you will learn how to implement centroid tracking with OpenCV, an easy to understand, yet highly effective tracking algorithm. This is a tall order for any computer vision or image processing algorithm and there are a variety of tricks we can play to help improve our object trackers.īut before we can build such a robust method we first need to study the fundamentals of object tracking. Be able to pick up objects it has “lost” in between frames.Be able to handle when the tracked object “disappears” or moves outside the boundaries of the video frame.Will be extremely fast - much faster than running the actual object detector itself.Only require the object detection phase once (i.e., when the object is initially detected). ![]() Object tracking is paramount to building a person counter (which we’ll do later in this series). And then tracking each of the objects as they move around frames in a video, maintaining the assignment of unique IDsįurthermore, object tracking allows us to apply a unique ID to each tracked object, making it possible for us to count unique objects in a video.Creating a unique ID for each of the initial detections.Taking an initial set of object detections (such as an input set of bounding box coordinates).Today’s tutorial kicks off a new series of blog posts on object tracking, arguably one of the most requested topics here on PyImageSearch. Click here to download the source code to this post ![]()
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