Fine-grained Categorization of Fish Motion Patterns in Underwater Videos

1. Fine-grained Categorization of Fish Motion Patterns in Underwater Videos Mohamed Amer, Emil Bilgazyev, Sinisa Todorovic, Shishir Shah, Ioannis Kakadiaris, Lorenzo Ciannelli Results (1) Detection Problem Statement Biologically inspired problem Sea depth Time of video acquisition Fish behavior Input frames Given an underwater video of flat fish on the sea bed Analyze fish behavior Estimate sea depth and time of video acquisition Challenges Magnitude and phase of optical flow Low contrast Low resolution Motion blur Large intra-class variability Whirls of sand Very similar classes of fish behavior Our Approach (1)Detection of fish occurrences Clusters of optical flow … Input Frames Detections Tracking (2) Tracking by detection Average detection results on our underwater video dataset, and the comparison with the three state-of-the-art detectors. Background subtraction and removing noisy clusters (2) Tracking (3) Classification of fish tracks (Six classes of fish behavior) Detections are nodes in a weighted graph Tracking = Max weighted clique estimation Class 1: depth=30m, time=morning Class 2: depth=40m, time=morning Class 3: depth=50m, time=morning Class 4: depth=30m, time=afternoon Class 5: depth=40m, time=afternoon Class 6: depth=50m, time=afternoon CLEARMOT results on our underwater video dataset, and comparison with the state-of-the-art tracker. Detection (3) Classification References [1] B. Leibe, A. Leonardis, and B. Schiele IJCV08 [2] N. Dalal and B. Triggs CVPR05 [3] P. Felzenszwalb, R. Girshick, and D. McAllester CVPR10 [4]M. D. Breitenstein, F. Reichlin, B. Leibe, E. Koller-Meier and L. V. Gool ICCV09 Features = log-polar histograms of fish motion patterns Classifier = Random Forest