---
language:
- en
license: cc-by-nc-nd-4.0
task_categories:
- image-to-image
- object-detection
tags:
- code
- biology
dataset_info:
- config_name: video_01
  features:
  - name: id
    dtype: int32
  - name: name
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: shapes
    sequence:
    - name: track_id
      dtype: uint32
    - name: label
      dtype:
        class_label:
          names:
            '0': dog
    - name: type
      dtype: string
    - name: points
      sequence:
        sequence: float32
    - name: rotation
      dtype: float32
    - name: occluded
      dtype: uint8
    - name: attributes
      sequence:
      - name: name
        dtype: string
      - name: text
        dtype: string
  splits:
  - name: train
    num_bytes: 14990
    num_examples: 52
  download_size: 313328015
  dataset_size: 14990
- config_name: video_02
  features:
  - name: id
    dtype: int32
  - name: name
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: shapes
    sequence:
    - name: track_id
      dtype: uint32
    - name: label
      dtype:
        class_label:
          names:
            '0': dog
    - name: type
      dtype: string
    - name: points
      sequence:
        sequence: float32
    - name: rotation
      dtype: float32
    - name: occluded
      dtype: uint8
    - name: attributes
      sequence:
      - name: name
        dtype: string
      - name: text
        dtype: string
  splits:
  - name: train
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    num_examples: 58
  download_size: 67354761
  dataset_size: 19600
- config_name: video_03
  features:
  - name: id
    dtype: int32
  - name: name
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: shapes
    sequence:
    - name: track_id
      dtype: uint32
    - name: label
      dtype:
        class_label:
          names:
            '0': dog
    - name: type
      dtype: string
    - name: points
      sequence:
        sequence: float32
    - name: rotation
      dtype: float32
    - name: occluded
      dtype: uint8
    - name: attributes
      sequence:
      - name: name
        dtype: string
      - name: text
        dtype: string
  splits:
  - name: train
    num_bytes: 14126
    num_examples: 49
  download_size: 148412090
  dataset_size: 14126
---
# Dogs Video - Object Detection Dataset

The dataset contains frames extracted from videos with dogs on the streets. Each frame is accompanied by **bounding box** that specifically **tracks the dog** in the image. 

# 💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on **[TrainingData](https://trainingdata.pro/datasets/object-tracking?utm_source=huggingface&utm_medium=cpc&utm_campaign=dogs-video-object-tracking-dataset)** to buy the dataset

The dataset provides a valuable resource for advancing computer vision tasks, enabling the development of more accurate and effective solutions for monitoring and understanding dog behavior in urban settings.

![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F2d6ca1d0561d6eaf77f60743335d4e03%2F3cb2d54c-7dd7-4c05-8764-bf2156e90381.gif?generation=1695301016829163&alt=media)

# Dataset structure
The dataset consists of 3 folders with frames from the video with dogs on the streets. 
Each folder includes:
- **images**: folder with original frames from the video,
- **boxes**: visualized data labeling for the images in the previous folder,
- **.csv file**: file with id and path of each frame in the "images" folder,
- **annotations.xml**: contains coordinates of the bounding boxes, created for the original frames

# 💴 Buy the Dataset: This is just an example of the data. Leave a request on **[https://trainingdata.pro/datasets](https://trainingdata.pro/datasets/object-tracking?utm_source=huggingface&utm_medium=cpc&utm_campaign=dogs-video-object-tracking-dataset)** to discuss your requirements, learn about the price and buy the dataset

# Data Format

Each frame from `images` folder is accompanied by an XML-annotation in the `annotations.xml` file indicating the coordinates of the bounding boxes for dogs tracking. For each point, the x and y coordinates are provided.

# Example of the XML-file 
![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F8efa058042b600f842fbb76da35c4876%2Fcarbon%20(1).png?generation=1695994709378514&alt=media)

# Object tracking might be made in accordance with your requirements.

# **[TrainingData](https://trainingdata.pro/datasets/object-tracking?utm_source=huggingface&utm_medium=cpc&utm_campaign=dogs-video-object-tracking-dataset)** provides high-quality data annotation tailored to your needs

More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**

TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**

*keywords: object movements, dogs behavoir, pets object detection, motion tracking, domestic dogs, stray dogs, object detection, multiple-object tracking, image dataset, labeled web tracking dataset, computer vision, machine learning, cctv, camera detection, surveillance, security camera, security camera object detection, video-based monitoring, smart city, smart city development, smart city vision, smart city deep learning, smart city management, classification, image recognition*