People detection and optional tracking with Tensorflow backend.
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Updated
Aug 9, 2024
People detection and optional tracking with Tensorflow backend.
The easiest way to count pedestrians, cyclists, and vehicles on edge computing devices or live video feeds.
This tracker is based on the use of a detector in the form of a YOLOv5s neural model and a tracking algorithm for tracking objects (DeepSORT).
A really more real-time adaptation of deep sort
✌️ Detection and tracking hand from FPV: benchmarks and challenges on rehabilitation exercises dataset
A fish viewer application that uses deep learning models to detect fish types and the length of fish using an image, video or a camera input.
Deepsort with yolo series. This project support the existing yolo detection model algorithm (YOLOV8, YOLOV7, YOLOV6, YOLOV5, YOLOV4Scaled, YOLOV4, YOLOv3', PPYOLOE, YOLOR, YOLOX ).
yolov8 with DeepSort_Tracking
MOT using deepsort and yolov7 with c++. It also supports yolov5 as a detector.
Approaching Pedestrian Tracking problem on surveillance camera with YoloV5 for pedestrian detection and DeepSORT for tracking.
Acquiring the demographic details such as Age and Gender of a person from a Surveillance Camera video using a custom trained CNN model.
People detection and optional tracking with Tensorflow backend.
Implementation of various methods of single / multi object tracking 🐾🛰
Deep SORT + YOLOv3, Tensorflow, Keras, OpenCV
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