E K O R T S N S

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E K O R T S N S IO I T N C N

E K O R T S N S IO I T N C N E E T ET 5 6 D S 3 C Mentor: Prof. Amitabh Mukherjee Khushdeep Singh (10351) Aakash Verma (10002)

APPLICATIONS OF HUMAN ACTIVITY RECOGNITION 1 1 2 20 03 2 - KHUSHDEEP SINGH

APPLICATIONS OF HUMAN ACTIVITY RECOGNITION 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA 2

E B E N H A C T O K T S N A

E B E N H A C T O K T S N A I T D NG I R E U ID W S O IV O SK L D L TA FO UB 1. S 1 1 2 20 03 2 - Player Tracking 2. Optical Flow Analysis 3. Using Motion Descriptors 4. Adaptive Boosting KHUSHDEEP SINGH AND AAKASH VERMA 3

T R L G I E F NLE I Y K A IC T

T R L G I E F NLE I Y K A IC T C L R P A PA R T ING U S E R

WHAT MUST BE SPECIFIED: • Prior Distribution: p(x 0) - Describes initial distribution of

WHAT MUST BE SPECIFIED: • Prior Distribution: p(x 0) - Describes initial distribution of object states • Transition Model: p(xt | xt-1) - Specifies how objects move between frames - A simple model: sample next state from a Gaussian window around current state - We used second order auto regressive model. xt = Axt-1 + Bxt-2 +wt • Observation Model: p( yt | xt ) - Color Histogram Object Tracking and Particle Filtering by Rob Hess [2006] 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA 5

EXAMPLE OF PLAYER TRACKING 1 1 2 20 03 2 - KHUSHDEEP SINGH AND

EXAMPLE OF PLAYER TRACKING 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA 6

W O M L F TH R L A GO I L C A

W O M L F TH R L A GO I L C A I L T K P OS IN G U 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA 7

OPTICAL FLOW ESTIMATION SPARSE OPTICAL FLOW DENSE OPTICAL FLOW • Computed only at a

OPTICAL FLOW ESTIMATION SPARSE OPTICAL FLOW DENSE OPTICAL FLOW • Computed only at a subset of image points. • Computed at each image pixel. • Quicker but less accurate results. • Slower but better results. • Example: Farneback Algorithm • Example: Kanade-Lucas Algorithm 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA 8

EXAMPLE OF SPARSE OPTICAL FLOW ESTIMATION 1 1 2 20 03 2 - KHUSHDEEP

EXAMPLE OF SPARSE OPTICAL FLOW ESTIMATION 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA 9

ITERATIVE KL OPTICAL FLOW COMPUTATION 1 1 2 20 03 2 - KHUSHDEEP SINGH

ITERATIVE KL OPTICAL FLOW COMPUTATION 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA 10

E A N S R E OO N I U T T J C

E A N S R E OO N I U T T J C A A L I E E O F ET V G N D SI LG O R IT H M U 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA 11

VOILA JONES ALGORITHM Efficient Visual Event Detection using Volumetric Features by Yan Ke, Rahul

VOILA JONES ALGORITHM Efficient Visual Event Detection using Volumetric Features by Yan Ke, Rahul Suthankar, Martial Herbert [ICCV’ 05] 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA 12

G N I T S O E V I T P A D A

G N I T S O E V I T P A D A M E T A M A C H IN E L E O B A R N IN G A L G O R IT H M 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA 13

1 1 02 2 03 2 Lecture on Ada. Boost by Jan Sochman, Jiri

1 1 02 2 03 2 Lecture on Ada. Boost by Jan Sochman, Jiri Matas KHUSHDEEP SINGH AND AAKASH VERMA 14

FOR THE GIVEN EXAMPLE 1 1 2 20 03 2 - KHUSHDEEP SINGH AND

FOR THE GIVEN EXAMPLE 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA 15

COMPARISON OF ADABOOST WITH OTHER METHODS Boos. Texter: A boosting-based system for text categorization

COMPARISON OF ADABOOST WITH OTHER METHODS Boos. Texter: A boosting-based system for text categorization by Robert E. Schapire and Yoram Singer. 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA 16

ADABOOST PSEUDOCODE 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA

ADABOOST PSEUDOCODE 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA 17

SOME INITIAL RESULTS USING ONLY 1000 WEAK CLASSIFIERS No. of Training Examples No. of

SOME INITIAL RESULTS USING ONLY 1000 WEAK CLASSIFIERS No. of Training Examples No. of Features Misses False Alarms 200 2, 000 3% 60% 200 2, 000 19. 58% 30% 500 1, 000 12% 58% * TRAINED ON THE KTH DATASET 1 1 2 20 03 2 - KHUSHDEEP SINGH AND AAKASH VERMA 18