This document summarizes a paper on CenterTrack, a new object tracking method. CenterTrack localizes objects as keypoints and associates them across frames with minimal input. It applies a detection model to pairs of images and prior detections to localize objects and predict associations. CenterTrack represents an improvement over prior methods by using keypoints instead of bounding boxes and requiring only previous detections as input for tracking. An ablation study showed that offset prediction and multi-scale feature maps were important for performance.

1. 1
DEEP LEARNING JP
[DL Papers]
http://deeplearning.jp/
Tracking Objects as Points
Shizuma Kubo, ACES.Inc

2. • mhn
Ø 2I CA 0: K 1GA K
Ø tj A A GL 4 G KL 1 A AHH I :L
Ø l 2 (L KA K / :
Ø G I A
Ø ( ) Xa c ( ( ud
Ø io . / /
Ø GIE Kv ))4 uk [TO P
s b] rXfgXVOep ry AK,L:rXZXU

3. • ( ( ( ( ) r h fc djgl
Ø (s D A : 7E - D0 7E / ( :AC 7 DC
Ø e il P M t . - J P Mjgl L
Ø woSa M J J
Ø E A7 7 CE h fc djgl MN
• n E2 e il O p TJ136 M b
136 ) u 36

4. .5
.5
32
1 4

5. Ø
Ø 5 32
Ø .
4
4 C
.

6. Multi Object Tracking (MOT)
• Ro k c jTmd S[
• l jTmd i Sr DS
Ø W awT] I M S
Ø O g
• l p p Tt Sr DS
Ø W jTne st S r
Ø O g
h +6: 6 2 6: 1 6 A6 16 6 61 7

7. MOT Challenge
• O i N P ( 177 x MP O M(0-
M
• lO N 199 2 M
• 2 2 0 N 1 2 2 2 0 N Cn V
Ø 2 2 0 e g )1 : ( Os r N i
Or V MPF T d i
Ø 1 2 2 2 0 s pR r V
• O ah O O O M VFN
• O M tVO

8. %
• ) i ( ( gT D : P gs v
a S
• o e ) ISNI W F : g s
• % e T G D o g
• e T G D o g
• (0 1 81
• (0 1 120 81
• F D oT P tF D l

9. MOT
• - - ng M r 9
9 : M i c :
Ø a Mt :
Ø M P 9 T M9
b PO
• - - - - ng M
: i c : u D :
Ø e] 9 xM
Ø ( Pv ) ng [ ] do l b M T9
e]

10. MOT : Tracking-by-detection
• 1 6
Ø [r ) 0 u eBa a
c ]B gj
Ø Tr jdi ʼ
• 1
Ø c ]B gj a I [r
Ø [r n
• ( - 01 1 i b W yx P lx c ]B gj
[k ow t UW C

11. MOT : Joint detection and tracking
• VekFhlF IgFb
Ø 9 9 8 7 7 C
Ø 8 7 D
Ø + 98 B8 7 1 B78 C
• ]Z ekFh ʻ V oI uT wr ]
V sTS ʼ V I P
• nt V Vdic h I [amf Tt n

12. )(
• nr 1 0 Emr ) D
v c ea
• 6 27 6 ) D
( M A
• 61 70 c 0 706 6 2t
D C
• ) D o P
TD V i
k O
)
)
0 2

13. Limitation
• W 3
TO S f
• o7 3 3
TO S f
D I6
• L k 1 1
3 e
M

14. Ø . .
Ø .
435
1

15. CenterTrack
• Our tracker, CenterTrack, applies a detection model to a pair of images and
detections from the prior frame. Given this minimal input, CenterTrack
localizes objects and predicts their associations with the previous frame.
That’s it.

16. CenterTrack
• 1 (1( 1 a e
f
• a e Tc g a
d
Ø i J 6
Ø a kT )) (1 6

17. CenterNet (Objects as Points)
width
• C e a 7=c
+ )22 1+) 1 2 1 + 2 4
• b a C
• C ( L
height
: https://medium.com/machine-learning-bites/deeplearning-series-convolutional-neural-networks-a9c2f2ee1524

18. CeterTrack
• N e ( 2 1) 2 N e
• ( 2 1) 2 1
:C ( 2 1) 2
• ( 2 1) 2 8 :C ( 2 1) 2
• N 3 + 4 :Cd 3 + 4 b
C

19. 15
Ø 9
Ø 25 . 4

20. + ( + )
• e CN : 1 2 :
Ø :
Ø :
•
01

21. (offset prediction)
• ri c no
Ø ) )1 1 ) )1 s
Ø , +) 1() ) ) )
Ø N2 f e e c : c
Ø +() )d c
Ø t c C
1
) ) ) )
)

22. Ablation study
• / 2 o h ar n mD C
• l gp N : f
Ø i e
• h SW D I : f
Ø i e

23. offset prediction ( )
• l t R 2T a R 2T
c R 2T p :
• f e st 3 i o
Ø p :
Ø O S R 2 w
Ø c

24. 315
315
Ø .2 4
Ø

25. • F 5 n m 5 Fi N
m t
• v :F o N F F P e A 2 ) (
Ø F
Ø ( ) ) s u 5 l A
Ø ( ) ) ( ) s
• 5 5 Fg v F 5 a F

26. • a e [ p
Ø [ ]
Ø 2 2() i c
• T k
Ø l a e [Tf c g : r
s Tyu
Ø vn 2 2 ) 6
Ø tn t C 2() [ ]
[ ] T

27. Ablation study
• a i du ) (
Ø ) ( t
Ø 3 3 ) ( :
• m e a
Ø r : 2 r r
• . o 3 ) (( : e
Ø n A 7e g
Ø ri :

28. .
. 43
2 15

29. • 2
Ø 2 s
Ø ( ) 2 oe
• C : 9
Ø I D
Ø 2 D
• C f