Object Counter

University of Glasgow, MSc. Thesis Project

This project consists of a set of experiments to compare the performance of combinations of popular and state-of-the-art object detection models and multi-object tracking algorithms. All combinations are evaluated on the Town Centre Dataset and the MOT17 dataset with the objective of tracking and counting the pedestrians crossing a LOI in a video sequence (MOT). New evaluation datasets will be added.

Project Structure

.
├── detectors/                      # Pre-trained person detection models files and frozen TF graphs
├── trackers/                       # Multi-object tracking algorithms
├── utils                           # Utility functions to process inputs and video frames
    └── file_utils.py
    └── image_utils.py
    └── model_utils.py                          
    └── math_utils.py
    └── label_map_util.py                  
    └── get_input_args.py
├── protos/                         # Tensorflow Object Detection API protos
├── input_videos/                   # Input videos
├── output/                         # Output videos                         
├── ground_truth                    # Ground truth data for the Oxford Town Centre Dataset
├── run_sort.py                     # Runs SORT
├── run_deepsort.py                 # Runs DeepSORT
├── groundtruth.py                  # Generates Oxford Town Centre video with ground truth annotations
├── requirements.txt                # Dependencies
└── README.md

Installation

Input arguments and options:

• input: path to input video
• output: path to output folder
• model: object detection model
• dlib: use kernelized correlation filter or Kalman Filter - True or False
• confidence: confidence threshold for object detection - default is 0.5
• threshold: non-maxima suppression threshold - default is 0.3
• line: automatically assign ROI, use user input to draw a boundary line on frame or manually enter line coordinates - select 0, 1 or 2

--line 1 opens a visual interface to draw a line on the input video, --line 2 prompts user to enter line coordinates: x1 y1 x2 y2.

Run the script: python run_sort.py --model [DETECTION_MODEL]--input [INPUT_VIDEO_PATH] --output [OUTPUT_FOLDER] --line 0
python run_deepsort.py --model [DETECTION_MODEL]--input [INPUT_VIDEO_PATH] --output [OUTPUT_FOLDER] --line 0

Available Detection Models

• YOLOv3 can be loaded using the model argument: --model yolo
• TensorFlow models pretrained on the COCO dataset. A full list of the available models can be seen at Tensorflow Model Zoo.
• HAAR Cascade for full body detection: --model haar
• HOG with Linear SVM: --model hog

To use the TensorFlow models select a model from the model zoo and pass it as the model argument: --model ssdlite_mobilenet_v2_coco. This automatically creates a folder in detectors/ and downloads the model files if theey don't exist.

Use --model ground-truth to track with ground truth detections. ground-truth option uses the annotations of the Town Centre Dataset.

Available Trackers

• SORT with Kalman Filter
• SORT with Correlation Filter
• DeepSORT

DeepSORT code is largely borrowed from Nikolai Wojke's DeepSORT repo.