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https://github.com/abdur75648/ai-camera

AI Camera: High-Performance Real-Time Object Detection & Tracking
https://github.com/abdur75648/ai-camera

artificial-intelligence deepsort deepsort-tensorrt person-tracking real-time-monitoring tensorrt yolo yolo-tensorrt yolov8

Last synced: 8 months ago
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AI Camera: High-Performance Real-Time Object Detection & Tracking

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README 

          
# AICamera: Real-Time High-Speed Person Detection & Tracking





  AICamera Demo




## 🚀 Overview



**AICamera** is a high-performance, real-time object detection and tracking system built as part of a larger Computer Vision pipeline during a project at **ExaWizards Inc.** It focuses on accurate **person detection and tracking**, optimized for deployment on NVIDIA GPUs using **TensorRT**. The system leverages the speed and accuracy of **YOLOv8** for detection and combines it with **DeepSORT** for robust multi-object tracking.



This submodule was designed to act as a core engine in downstream applications such as real-time surveillance, retail analytics, and smart camera systems — with a strong emphasis on efficiency, modularity, and real-world deployability.



---



## 🧠 Key Technologies



* **🔍 Detection:** [YOLOv8](https://docs.ultralytics.com/models/yolov8/) — state-of-the-art real-time object detection model by Ultralytics.

* **🧭 Tracking:** [DeepSORT](https://arxiv.org/abs/1703.07402) — combines motion (Kalman filter) and appearance (ReID) features for reliable tracking.

* **⚡ Acceleration:** [NVIDIA TensorRT](https://developer.nvidia.com/tensorrt) — significantly reduces inference time by optimizing ONNX models to run on NVIDIA GPUs.



---



## ⚙️ How It Works



1. **Input Capture:** Reads video frames from a file or webcam using OpenCV.

2. **Preprocessing:** Resizes and normalizes images for inference.

3. **Detection (YOLOv8 + TensorRT):** Outputs class labels, bounding boxes, and confidence scores.

4. **Tracking (DeepSORT + TensorRT ReID):**



   * ReID crops extracted from detected persons.

   * TensorRT-optimized ReID model generates embeddings.

   * Kalman filter + cosine distance + IoU matching for identity preservation.

5. **Visualization:** Annotated video with object IDs and bounding boxes.



---



## ✅ Features



* 🚶‍♂️ **Focused on Person Tracking**

* ⚡ **Blazing Fast** due to TensorRT acceleration

* 🔁 **Modular Pipeline** with clean interfaces for detection, tracking, and I/O

* 🧪 **Easily Configurable:** Adjust thresholds, engine paths, and target classes

* 🖥️ **Supports Webcam & Video Input**

* 📂 **Out-of-the-box Setup:** Includes helper scripts for model downloading and engine conversion



---



## 🖥️ System Requirements



* **OS:** Ubuntu 22.04 LTS (or compatible)

* **GPU:** NVIDIA CUDA-enabled GPU (Compute Capability ≥ 6.1)

* **NVIDIA Stack:**



  * NVIDIA Driver (latest)

  * CUDA Toolkit ≥ 12.1

  * cuDNN ≥ 9.0

  * TensorRT ≥ 10.x (Ensure `trtexec` is available)

* **Python:** 3.10.x



---



## 📦 Installation



### 1. Clone Repository



```bash

git clone https://github.com/abdur75648/AI-Camera.git

cd AI-Camera

```



### 2. Ensure NVIDIA Stack is Installed



Ensure you have the NVIDIA driver, CUDA, cuDNN, and TensorRT installed. You can check if they are installed correctly by running:



```bash

nvidia-smi

```

This should show your GPU information.



Follow [NVIDIA's official documentation](https://docs.nvidia.com/) for installing the driver, CUDA, cuDNN, and TensorRT, if not already installed.



Ensure:



```bash

nvcc --version

trtexec --help

```



### 3. Install Python Dependencies



```bash

pip install --upgrade pip

pip install -r requirements.txt

```



### 4. Download ONNX Models



```bash

bash scripts/download_models.sh

```



### 5. Export TensorRT Engines



```bash

bash scripts/export_trt_engines.sh

```



This creates:



* `models/detection/yolov8n.engine`

* `models/reid/deepsort_reid.engine`



---



## ▶️ Usage



Run on a video file:



```bash

python3 -m src.aicamera_tracker --input sample_input/sample_video.mp4 --show_display

```



Run on webcam:



```bash

python3 -m src.aicamera_tracker --webcam_id 0 --output_filename outputs/webcam_run.mp4 --show_display

```



Run with custom confidence threshold:



```bash

python3 -m src.aicamera_tracker --input video.mp4 --conf_thresh 0.4

```



### 🔧 Common CLI Options



| Argument            | Description                                 |

| ------------------- | ------------------------------------------- |

| `--input`           | Path to input video file                    |

| `--webcam_id`       | Webcam ID (default: 0)                      |

| `--output_dir`      | Output directory (default: `outputs/`)      |

| `--output_filename` | Output filename (auto-generated if not set) |

| `--show_display`    | Show live OpenCV display                    |

| `--no_save`         | Skip saving output video                    |

| `--yolo_engine`     | Path to YOLOv8 TensorRT engine              |

| `--reid_engine`     | Path to ReID TensorRT engine                |

| `--conf_thresh`     | Confidence threshold for detections         |

| `--device`          | Inference device (default: `cuda:0`)        |



---



## ⚡ Performance



| Component           | Raw Engine Speed (GTX 1660Ti) | Notes                          |

| ------------------- | ----------------------------- | ------------------------------ |

| YOLOv8n (TRT)       | \~400+ FPS                    | Highly optimized inference     |

| ReID (TRT)          | \~600+ FPS                    | Fast identity embedding        |

| End-to-End Pipeline | \~30 FPS                   | Varies by resolution, #objects |



> ⚠️ Enabling `--show_display` may reduce FPS due to rendering overhead. Disable for benchmarking.



---



## ⚙️ Configuration



Update `src/config.py` to modify:



* **Paths:**



  * `YOLO_ENGINE_PATH`, `REID_ENGINE_PATH`

* **YOLO Settings:**



  * `YOLO_INPUT_SHAPE`, `YOLO_CONF_THRESHOLD`

* **DeepSORT Settings:**



  * `DEEPSORT_MAX_DIST`, `DEEPSORT_MAX_AGE`, etc.

* **Classes to Track:**



  * `CLASSES_TO_TRACK = {'person'}`



---



## 🧱 Project Structure



```

AICamera/

├── assets/              # Demo assets

├── models/              # Pretrained models (.onnx, .engine)

│   ├── detection/

│   └── reid/

├── scripts/             # Scripts for model setup

├── sample_input/        # Sample videos/images

├── src/

│   ├── aicamera_tracker.py

│   ├── config.py

│   ├── detector/        # YOLOv8 wrapper

│   ├── tracker/         # DeepSORT tracker

│   │   └── core/        # Core DeepSORT logic

│   ├── utils/           # Helper utilities

│   └── trt_utils/       # TensorRT engine handling

└── requirements.txt

```



---



## 🔮 Future Enhancements



* Support for other YOLOv8 sizes (s, m, l, x)

* Integration with other tracking algorithms (e.g., ByteTrack, OC-SORT)

* Smarter gallery management in ReID

* Asynchronous pipeline for faster I/O

* MOT evaluation metrics (MOTA, MOTP)

* Batch-mode frame processing



---



## 📄 License



Licensed under the [MIT License](LICENSE). You're free to use, modify, and distribute with attribution.



---



## 🙏 Acknowledgements



* [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics)

* [DeepSORT](https://github.com/nwojke/deep_sort)

* [NVIDIA TensorRT](https://developer.nvidia.com/tensorrt)