7/28/2023 0 Comments Multi counter computerMost of the apps available on Google play store or iOS Appstore are made exclusively for mobile platforms. Keep reading this article to get to know how you can Download and Install one of the best Tools App TickTap+ (Multi counter) for PC. If environment.yml isn't working, try environment_windows.yml if you're on a Windows machine.Looking for a way to Download TickTap+ (Multi counter) for Windows 10/8/7 PC? You are in the correct place then. You can use the conda environment file to set up all dependencies. This project was built and tested on Python 3.6. Using YOLO v4 made it much easier to run two streams with a higher resolution, as well as giving a better detection accuracy. I used YOLO v3 when I first started the object counting project which gave me about ~10FPS with tracking, making it difficult to run more than one stream at a time. Of course, this heavily depends on stream resolution and how many frames are being processed for detection and tracking. To give some idea of what do expect, I could run two traffic counting streams at around 10fps each (as you can see in the traffic counting gif). It's been good enough though for my use case so far. not enough images) are discarded after considering occlusion and truncation ratios.ĭETRAC images are converted into the Darknet YOLO training format.īoth models were trained and evaluated on the DETRAC training set, but no evaluation has been done yet on the test set due to lack of v3 annotations and I don't have MATLAB for the Deep SORT evaluation software. Number of occurrences - vehicle sequences that are too short (i.e.Truncation threshold - ignore vehicle sequences with too high truncation ratio.Occlusion threshold - ignore vehicle sequences with too high occlusion ratio.Deep SORT conversion parametersĭETRAC images are converted into the Market 1501 training format. However, if your goal is to use this app to count people then this shouldn't be much of an issue. This is mainly because the original Deep SORT model (mars-small128.pb) was trained on tracking people and not vehicles. Please note that if you decide not to train your own models, the vehicle tracking performance will most likely be worse than if you trained your own models on the DETRAC dataset or any other traffic dataset. Follow the instructions in the Keras-to-YOLOv4 repository for downloading and converting an already trained Darknet YOLO v4 model to Keras format.Alternatively, you can also find these in my Deep SORT and YOLOv4 repository If you don't want to train your own models, this repository already includes the trained Deep SORT model (mars-small128.pb) from the original Deep SORT repository. The Deep SORT model was trained using cosine metric learning. I had to train the YOLO v4 model using Darknet and then convert it to Keras format using convert.py from the Keras-to-YOLOv4 repository. I've provided the scripts for converting the DETRAC training images and v3 annotations into the correct format for training both the YOLO v4 model as well as the Deep SORT tracking model. I trained a YOLO v4 and Deep SORT model using the DETRAC training dataset with v3 annotations. Training your own vehicle tracking model ( Link) Below shows detection, tracking, and counting of people and cars. This project was originally intended to be an app for counting the current number of people in multiple rooms using my own smartphones, where the server would be remotely hosted. For example, it can frequently misclassify hatchbacks as SUVs, or not being able to detect taxis due to different colour schemes. Additionally, the DETRAC dataset only contains images of traffic in China, so it struggles to correctly detect certain vehicles in other countries due to lack of training data. Note that since DETRAC doesn't contain any motorcycles, they are the only vehicles that are ignored. Records intersection details for each counted object.Records counts for every set interval of the hour.Directional counts can be configured based on angle.Video streaming possible via emulated IP camera.Showing detections is optional (but hides average detection confidence).Tracked classes determined by most common detection class.Tracked objects show average detection confidence.Tracked using low confidence track filtering from the same paper.Hence, those that lose tracking but are retracked with the same ID still get counted.Counts objects by looking at the intersection of the path of the tracked object and the counting line.I used this paper as a guideline for data preparation and training.You can find the conversion code that I created here. Trained using a total of 244,617 images generated from the DETRAC dataset.
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