This repo provides function call to track multi-objects in videos

Overview

Custom Object Tracking

Introduction

This repo provides function call to track multi-objects in videos with a given trained object detection model and a source video file as inputs. The tracking approach used in the repo is DeepSort - Simple Online and Realtime Tracking with a Deep Association Metric

Installation

Please install Tensorflow 2 Object Detection API and add the path to your environment.

Usage

1. Clone the github repository

git clone https://github.com/sek788432/Custom_Object_Tracking.git

2. Execution (In two ways)

  • saved_model_path: your own object detection model ckpt path
  • test_path: test video path
  • label_map_path: label_map.pbtxt path
  • deep_sort_model: deep sort model path
  • output_path: output video path
  • min_score_thresh: the minimum score threshold of object detection model
  1. Run tracking.py
  • e.g.
    cd  Custom_Object_Tracking/objectTracker/
    python tracking.py \
        --saved_model_path=exported-models/ssd_resnet50_119ckpt \
        --test_path=test_video.mp4 \
        --label_map_path=label_map.pbtxt \
        --deep_sort_model=data/mars-small128.pb \
        --output_path=test_video_tracking.mp4 \
        --min_score_thresh=.5
  1. Call TrackVideo function
  • e.g.
    from Custom_Object_Tracking.objectTracker.tracking import TrackVideo
    TrackVideo(label_path, model_path, video_path,
                   output_path, threshold, deep_sort_model)

Result

Tracking vehicle by our own model (SSD ResNet50 trained on Waymo Dataset)

Vehicle Tracking

Owner
Jeff Lo
Jeff Lo
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