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[DL輪読会]Tracking Objects as Points

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Deep Learning JP
Deep Learning JP

2020/04/17 Deep Learning JP: http://deeplearning.jp/seminar-2/

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1 DEEP LEARNING JP [DL Papers] http://deeplearning.jp/ Tracking Objects as Points Shizuma Kubo, ACES.Inc
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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
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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.
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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
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Ablation study • / 2 o h ar n mD C • l gp N : f Ø i e • h SW D I : f Ø i e
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[DL輪読会]Tracking Objects as Points

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