Leaderboard Scene Parsing Car Instance Lane Segmentation Localization Trajectory Detection Tracking Stereo

Rank
Method
Score
CVPR Challenge rank 1
Megvii
0.3399
CVPR Challenge rank 2
Smart_Vision_SG
0.3022
CVPR Challenge rank 3
SZU_N606
0.2502
4
Autopilot
0.24968
5
ChiZhouMeiziHao
0.24475
6
xiaoming
0.21865
7
P_Beta
0.21546
8
xiteng
0.20405
9
iRedRum
0.20327
10
zwhh
0.20316
11
William Wang
0.19895
12
Mhttx
0.19817
13
G934
0.19800
14
InnerPeace
0.19709
15
dishen
0.19348
16
tkuanlun350
0.18913
17
Azat Akhtyamov
0.18362
18
SeetaTech
0.18122
19
svendroste
0.18064
20
AlbertoCastaño
0.17734
21
Holy Fit
0.17105
22
kekedan
0.15647
23
xiaoyucoco1314
0.15463
24
lbin
0.15137
25
mingye
0.15065
26
See--
0.14077
27
Undecided
0.13146
28
Eyon
0.12914
29
Insight
0.11830
30
w_________g
0.11096
31
GodEye
0.11095
32
bnrc
0.11094
33
makesense
0.10855
Rank
Method
AP
AP50
AP75
APS
APM
APL
AR1
AR10
AR100
ARS
ARM
ARL
Rank
Method
Mean Iou
Rank
Method
Mean
Rank
Method
WSADE
ADEv
ADEp
ADEb
WSFDE
FDEv
FDEp
FDEb
baseline
TrafficPredict
8.5881
7.9467
7.1811
12.8805
24.2262
12.7757
11.1210
22.7912
Team： Yuexin Ma, Xinge Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, and Dinesh Manocha; BaiduRAL
Method Description：Based on LSTM, our approach uses an instance layer to learn instances’ movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction.
Publication：TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents [PDF]. AAAI(oral), 2019
Rank
Method
mAP
AP(Car)
AP(Pedestrian)
AP(Cyclist)
baseline
cnnseg
44.974
55.926
35.398
43.598
Rank
Method
MOTAm
MOTA(Car)
MOTA(Pedestrian)
MOTA(Cyclist)
baseline
IOU_tracker(based on cnnseg)
18.973
56.919
0
0
Rank
Method
D1_all
D1_fg
D1_bg