Deep Joint Discriminative Learning for Vehicle Re-identification and Retrieval

Transcription

1 Deep Joit Discrimiative Learig for Vehicle Re-idetificatio ad Retrieval Yuqi Li, Yaghao Li, Hogfei Ya, Jiayig Liu Pekig Uiversity

2 Deep Joit Discrimiative Learig for Vehicle Re-idetificatio ad Retrieval Outlie Backgroud Deep Joit Discrimiative Learig Experimetal Results Coclusio

3 Deep Joit Discrimiative Learig for Vehicle Re-idetificatio ad Retrieval Outlie Backgroud Deep Joit Discrimiative Learig Experimetal Results Coclusio

4 004 Backgroud Vehicle search ad re-idetificatio Yaghao Li

5 005 Backgroud Yaghao Li Vehicle search ad re-idetificatio Practical applicatios i video surveillace systems Challege Licese plate is ot clear Low-resolutio Occluded or removed àvehicle ReID based o appearace iformatio

6 006 Backgroud Yaghao Li Vehicle search ad re-idetificatio VehicleID dataset Labeled i idetity level Remove licese plate Hogye Liu et al. Deep relative distace learig: Tell the differece betwee similar vehicles, i CVPR, 2016.

7 007 Backgroud Yaghao Li Related work Most idetificatio works focus o face or perso Face recogitio Perso re-idetificatio Target: lear discrimiative represetatios State-of-art à Deep CNN based DeepID [Su et. al, 2014] Directly classify idetities (~1w) DeepID2 [Su et. al, 2014] Pairwise verificatio loss Triplet loss [Schroff et. al, 2015, Dig et. al 2015] Triplet relatioship betwee positive ad egative pairs

8 008 Backgroud Yaghao Li Related work Differece of vehicle idetificatio Previous works focus o model classificatio Recogize models istead of idetities Vehicles of same model à similar visual appearace Capture special marks

9 009 Backgroud Yaghao Li Related work Differece of vehicle idetificatio Previous works focus o model classificatio Recogize model istead of idetities Vehicles of same model à similar visual appearace Capture special marks Large scale vehicle idetificatio dataset VehicleID [Liu et al. 2016] Facilitate deep learig models

10 0010 Backgroud Yaghao Li Related work Differece of vehicle idetificatio Previous works focus o model classificatio Recogize model istead of idetities Vehicles of same model à similar visual appearace Capture special marks Large scale vehicle idetificatio dataset VehicleID [Liu et al. 2016] Facilitate deep learig models Deep Joit Discrimiative Learig (DJDL) model A uified framework to extract discrimiative features

11 Deep Joit Discrimiative Learig for Vehicle Re-idetificatio ad Retrieval Outlie Backgroud Deep Joit Discrimiative Learig Experimetal Results Coclusio

12 0012 Deep Joit Discrimiative Learig Yaghao Li Architecture Overview Uified framework for four tasks

13 0013 Deep Joit Discrimiative Learig Yaghao Li Network Architecture Uified framework for four tasks Shared base covolutio etwork A commo CNN pretraied o ImageNet Classificatio tasks Idetificatio Attribute recogitio Verificatio subetwork Two images Triplet subetwork Three images

14 0014 Deep Joit Discrimiative Learig Yaghao Li Network Architecture Idetificatio subetwork Each iput image à Idetity label Covetioal recogitio task Softmax + cross-etropy loss target label Predicted probability

15 0015 Deep Joit Discrimiative Learig Yaghao Li Network Architecture Attribute recogitio subetwork Joitly recogize vehicle attributes Such as color ad vehicle model

16 0016 Deep Joit Discrimiative Learig Yaghao Li Network Architecture Verificatio subetwork Pair-wise siamese etwork Use Euclidea distace after ormalizatio Distace à small if same idetity Distace à large ifdifferet idetity Margi parameter eforce distace > α

17 0017 Deep Joit Discrimiative Learig Yaghao Li Network Architecture Triplet subetwork Achor + positive + egative Margi parameter

18 0018 Deep Joit Discrimiative Learig Yaghao Li Traiig ad Optimizatio Objective fuctio SGD optimizatio Joitly learig i a sigle batch Specific batch compositio desig

19 0019 Deep Joit Discrimiative Learig Yaghao Li Traiig ad Optimizatio Batch compositio desig Satisfy four tasks at the same time Half positive pairs + half radom samples Positive pairs Radom samples

20 0020 Deep Joit Discrimiative Learig Yaghao Li Traiig ad Optimizatio Batch compositio desig Satisfy four tasks at the same time Verificatio samples Positive pairs Radom samples

21 0021 Deep Joit Discrimiative Learig Yaghao Li Traiig ad Optimizatio Batch compositio desig Satisfy four tasks at the same time Triplet samples Positive pairs Radom samples

22 0022 Deep Joit Discrimiative Learig Yaghao Li Vehicle Retrieval Discrimiative features à Build idex Vehicle Retrieval Nearest eighbor search Vehicle Image Discrimiative Feature Build Idex Fast Retrieval Deep CNN Fast Nearest Neighbor Search Marius Muja ad David G Lowe, Fast approximate earest eighbors with automatic algorithm cofiguratio, i VISAPP, 2009.

23 Deep Joit Discrimiative Learig for Vehicle Re-idetificatio ad Retrieval Outlie Backgroud Deep Joit Discrimiative Learig Experimetal Results Coclusio

24 0024 Deep Joit Discrimiative Learig Yaghao Li Experimetal settigs VehicleID Dataset images of vehicles Three test sets Small, medium, large size Two tasks Vehicle retrieval Vehicle re-idetificatio

25 0025 Deep Joit Discrimiative Learig Yaghao Li Experimetal settigs Implemetatio Details MXNet platform Base covolutioal etwork Iceptio-BN Augmetatio Radom crop Radom flip Batch size: 64 Margi parameters α, β as 0.9

26 0026 Deep Joit Discrimiative Learig Yaghao Li Vehicle Retrieval Evaluatio protocol Mea average precisio (MAP) Ablatio results

27 0027 Deep Joit Discrimiative Learig Yaghao Li Vehicle Retrieval Compare with state-of-art

28 0028 Deep Joit Discrimiative Learig Yaghao Li Vehicle Re-idetificatio Evaluatio protocols CMC curve

29 0029 Deep Joit Discrimiative Learig Yaghao Li Vehicle Re-idetificatio Evaluatio protocols Top1 ad Top 5 match rates

30 0030 Coclusio Yaghao Li Coclusio A ovel Deep Joit Discrimiative Learig model For vehicle re-idetificatio ad retrieval A uified framework by icorporatig four tasks Differet properties à beefit each other Joitly optimize specific desiged batch compositio Experimets validate the effectiveess of DJDL model State-of-the-art results o two tasks

31 0031 Thak you Yaghao Li pku.edu.c Project Page: