real time object tracking with yolov9 and deepsort algorithm

real-time object tracking with yolov9 and deep sort

object tracking is a crucial task in computer vision that involves detecting and following objects in a sequence of frames. in this tutorial, we will explore how to implement real-time object tracking using yolov9 for object detection and the deep sort (simple online and realtime tracking) algorithm for tracking those objects across frames.

prerequisites

1. **python**: ensure python 3.6 or higher is installed.
2. **libraries**: you will need the following libraries:
- `opencv-python`
- `torch` (for yolov9)
- `numpy`
- `scipy`

you can install them using pip:

3. **clone yolov9 repository**: you can find yolov9 on github. clone the repository and follow any installation instructions provided in the readme.

4. **deep sort implementation**: you can get deep sort from its github repository. clone it and install any required dependencies.

step-by-step implementation

1. **load yolov9 model**: first, load the yolov9 model for object detection.

2. **initialize video capture**: use opencv to capture video from a webcam or a video file.

3. **run object detection**: for each frame from the video, use yolov9 to detect objects.

4. **apply deep sort**: use the detections from yolov9 and feed them into the deep sort algorithm to track objects across frames.

5. **display results**: draw bounding boxes and labels on the output frames and display them.

code example

here is a simple example of how to implement the above steps:

explanation of the code:

1. **loading yolov9**:

2. **initializing deep sort**:
- we create an instance of the deep sort tracker.

3. **video capture**:
- we initialize opencv to capture video. change the parameter in `videocapture` to read from a file if needed.

4. **processing each frame**:
- for each frame captured, we resize it, run yolov9 to get detections, and prepare the bounding boxes ...

#ObjectTracking #YOLOv9 #numpy
Real time object tracking
YOLOv9
DeepSORT
object detection
computer vision
tracking algorithm
machine learning
video analysis
surveillance systems
multi-object tracking
deep learning
image processing
real-time analytics
AI tracking solutions
object recognition