Human tracking dataset captured by drone.

Introduction

• We have captured 500 videos by drone in various environment for multi object tracking task. Images were extracted from 30 frame per second video at different rate for training and test purposes. All extracted images are annotated by human with various properties such as occlusion, re-identification, visibility, pose, image truncation.

Label format

• Figure below is the example of ground truth file.

Position	1	2	3	4	5	6	7	8	9	10	11	12
	frame_number	person_id_index	tracking_id_index	box_top_left_x	box_top_left_y	box_width	box_height	is_valid	pose_class	occlusion	truncated	visibility

Description of properties

Key	Value type	Description
frame_number	INT	Image frame number
person_id	INT	Unique number assinged to person in the sequence (i.e., same person in the sequence will have same number regardeless of appear/disapper)
tracking_id	INT	Unique number assigned to track of person (i.e., when same person disapeared and appeared again in the sequence, this number will be different)
box_top_left_x	INT	X coordinate of top left corner
box_top_left_y	INT	Y coordinate of top left corner
box_width	INT	box width
box_height	INT	box height
is_valid	INT (0/1)	0 : invalud instance (e.g., human in the picture), 1 : valid instance
pose_class	INT (0/1/2)	0 : General pose, 1 : Sitting, 2 : Waving hands
occlusion	INT (0/1)	0 : occlusion , 1 : no occlusion
truncated	INT (0/1)	0 : truncated , 1 : no truncated
visibility	FLOAT	area of actual visible area in pixel / area of object size in the image predicted by human labeler

Download

• Download
  • Train images were extracted from video taken from drone at 1 frame per second.
    • This is for model training.
  • Test images were extracted from video taken from drone at 3 frame per second.
    • This is for evaluating the trained model.

Directory Structure

 [root path]/
 └──[test or train]/                  
    └──[seqence_(sequence number)]/
       ├──(sequence_number).txt     <--- ground truth
       └──[images]/
          └──(frame_number).png     <--- image

Visualizer

 python3 visualize_data.py --dir=[root_dir_path] --type=[train or test] --seq=[sequence number( 450 <= test < 500 ,  0 <= train < 450)]

key 'q' 'esc' : exit program
key 'n' '->'  : next frame
key 'p' '<-'  : prev frame

Data Distribution

Dataset

	Number of images	Description
train	13,500	Dataset used during the learning process
test	4,500	Dataset used only to assess performance
total	18,000

Drone camera angle

	Number of images	Description
general	14,370	Dataset taken when the angle of the drone camera to the ground is between 10~60 degrees
top	3,630	Dataset taken when the angle of the drone camera to the ground is around 90 degrees
total	18,000

Weather

	Number of images	Description
sunny	10,320	Dataset taken on a sunny day
cloudy	7,680	Dataset taken on a rainy or cloudy day
total	18,000

Filming location

	Number of images	Description
general	9,000	Dataset taken from common roads such as side walk and asphalt
green	6,900	Dataset taken in natural environments
play ground	2,100	Dataset taken from outdoor sports venues such as tennis courts and basketball courts
total	18,000

Class Distribution

Human pose class

	Number of objects	Description
general	134,328	Objects(people) with comman posture, such as walking or standing
sitting	9,598	Objects(people) with sitting posutre
waving hand	542	Object(people) waving hands
total	144,468

Occlusion

	Number of objects	Description
occluded	49,258	Objects that are obscured by other objects within the image
not occluded	95,210	Objects that appear completely without occlusion within the image
total	144,468

Truncation

	Number of objects	Description
truncated	10,508	Objects whose part is out of the image
not truncated	133,960	Objects that appear completely without truncation within the image
total	144,468

Data Example

• Images below are example images from dataset.

Acknowledgement

이 데이터는 2021년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원을 받아 수행된 연구의 결과물임 (No.171125972, 인명 구조용 드론을 위한 영상/음성 인지 기술 개발)

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.171125972, Audio-Visual Perception for Autonomous Rescue Drones)