In total, there were 239 annotated images in the final dataset. Boxing punch detector dataset in Roboflow Once the data is loaded into Roboflow, we have to annotate the images with bounding boxes around the images of my 4 classes: jab, cross, hook and uppercut. The next step was uploading the video into Roboflow and splitting it into 1 frame per second with images of poses in a set position to better train the model. To get started, we took video from a 12 minute boxing session to gather enough data to train a model. If you threw 50 punches in a round, you can challenge yourself to throw 60 the next round, and in turn get a better sweat in! No punch detection Creating and Annotation the Dataset from Images and Videos I thought it would be great if computer vision was able to capture the type of punches being thrown during a boxing workout round (jab, cross, hook, uppercut) as well as total punches in that round to determine what they need to work more on.įor example, if you want to get a better jab and are only throwing 5 jabs in a 3 minute round, this info should tell you that you need to throw more jabs!Īlso, knowing the total punches allows you to up your round intensity. I will start out saying this problem is probably better suited for keypoint detection, but object detection did the job! I often mix boxing workouts into my routine, and thought it would be a great place to start. As someone who loves to workout, I wanted to focus my project on a fitness related use case. One of the best parts about joining Roboflow is doing a computer vision project in your first 2 weeks.
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