Mobile Motion Tracking using Onboard Camera

Supervisor: Prof. LYU, Rung Tsong Michael Prepared by: Lam Man Kit

Wong Yuk Man

Outline

Motivations

Objective

Methods

Results

Future Work

Q&A

Motivations

Rapid increase in the use of camera-phone.

Commonly used for taking photos or capturing video only.

Is it possible to add more values to the camera and make full use of it ?

Motivations

Unlike traditional camera, camera-phone has more functions than just taking photos.

Camera-phone can perform image processing tasks on the device itself.

Symbian OS makes programming on mobile phones possible.

ObjectiveTo add more values to camera-phone, so that it enhances the human-computer interaction.Implement real-time motion tracking on Symbian phone, without requiring additional hardware.Movement detected acts as an innovative input method for different applications:

Camera mouse to control the cursorNew input method for interactive gamesGesture input

Objective

Novel ubiquitous computing applications can be developed !

Real-World Interaction

Motion Estimation

Motion estimation is a process to find the motion vector of the current frame from reference frame(s).

Optical flowBlock matching

Block matching algorithm is an integral part for most of the motion-compensated video coding standards. Eg MPEG 1, MPEG 2, H.263.

Block MatchingDivide the previous frame to small rectangular blocks.Find the best match for the reference block in current frame.Calculate motion vector between the previous block and its counterpart in the current frame.Typical size for a block: 16x16 pixels.Search Range W: typically 16 or 32 pixels.Similarity Measures:

Mean Absolute Error (MAE)Mean Square Error (MSE)Sum of the Absolute Difference (SAD)

SAD is used in our project.

Current frame

Previous frame

MV

Block Matching2W + 1

2BW + 1

2BH + 12H + 1

Search Window

Block in previous frame

Search Window (in current frame)

A region which has the same center as the selected block in the previous frame, extended by w pixels in both directions

BW = 1BH = 1W = 4H = 4

W

center pixel

Block-match Motion Estimation

Two kinds of methods commonly used:

Fast Search Algorithms• 2-D Logarithmic Search• 3-Step Search (3SS) • Diamond Search

Exhaustive Search Algorithm (ESA)

Fast Algorithms

Fast Search Algorithms:2-D Logarithmic Search3-Step Search (TSS) Diamond Search

Assumption:The matching error monotonically increases as the search position moves away from the optimal motion vector

Fast Algorithms - TSSThree-Step Search (TSS)

1st Step:• Search 8 surroundings

and the central point• Distance = w/2 pixels• Find the best match

2nd Step:• Use previous best match

as center• Repeat 1st step with

distance = w/4 pixels

3th Step:• Repeat 1st step with

distance = w/8 pixels

Searched only 25 points

1

1

11

11

1 1

1

23

2

2

222

2

2333 3 3

33

Center of Block

Search Window

1 2 3

Fast AlgorithmsAdvantages:

Extremely fast

Disadvantages:All fast algorithms greatly rely on a monotonically increasing match criteria around the location of the optimal motion vector

• Easily fall into local minimum

limited number of positions examined (only 25 points) inside the search window, only find suboptimal solution

Exhaustive SearchAll candidates within search window are examined(2w+1)2 positions should be examinedAdvantage: Good accuracy; Finds best matchDisadvantage: High computational load. Impractical for real-time applicationsSolution

Fast Exhaustive Search

2W + 1

2BW + 1

2BH + 12H + 1

Search Window

Block

W

Fast Exhaustive Block Matching Algorithms

Much Faster

No performance Loss

Idea: exclude many search positions while still finding best match:

SEA （ Successive Elimination Algorithm）PNSA （ Progressive Norm Successive Algorithm）

SEA and PNSA can be calculated quickly

SEA algorithmSAD of two blocks X and Y is defined as

By Minkowski inequality

Thus,

By calculating the block-sum difference first, we can eliminate

many candidate blocks (if D > SAD) before doing slow SAD

There exist fast method to calculate the block-sum for SEA

About 2 times faster than exhaustive search !!

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Fast

Slow

Denoted as D

Fast Exhaustive Block-Matching Algorithm

update

….

SAD SAD SAD

….

SAD….

Search range=2W+1

SEA

PNSA

The smallest SAD

Total No of candidate block: (2w+1)2

Probability of eliminating invalid candidate block:SEA < PNSA < SADComputation Load:SEA < PNSA < SAD

Feature SelectionWhich block should be chosen for tracking?

Flat-colored block is not good.

A block in a region of repeated pattern is not good.

Why is the “eye” a good candidate?It is a good tracking location because of the brightness difference between the black and skin colors.

How do we find a good feature block?Is that block good?

Is that block good?

No

No

It is a good block !!

Feature SelectionGoal:

Find a good reference block for tracking

Criteria:The candidate block should have great SAD with it’s neighborsIt contains “complex” information

Great SAD with neighbors blockPrevent ambiguous detectionSpeed up the searching algorithmMany candidate blocks are eliminated by the tree in upper level

Complex block Prevent choosing flat region as reference blockEnhance the performance of PDE (Partial Distortion Elimination)

PDE (Partial Distortion Elimination)

Simple, small overheadComparison can be done Halfway

Stop if the sub-blocks SAD between block X and Y is already larger than the previous minimum SAD

Removes unnecessary computations efficientlyif the feature block Y has high complexity

It will have great SAD with block XIncrease chance of halfway stop

We implement a simple feature selection algorithm based on the above criteria

X: candidate blockY: feature block

Feature SelectionDivide the current frame to small rectangular blocksFor each block, sum all the pixels value, denoted as Ixy (Intensity of the block)Calculate the variance of each block which represent the complexity of the blockUse Laplacian Mask for each block

The Laplacian operator indicates how the reference block differs from the neighbors

• Flat background > small output• Dissimilar with neighbors > large output

Select the block which has the largest Ixy and large variance as the feature block

Laplacian Mask

Adaptive Search Window

Conventional methodSearch window is defined as a rectangle with the same center as block in previous frame, extended by W pixels in both directions.

Search Window

Block

Center of Search Window

Adaptive Search WindowProposed method

Center of the search window is predicted based on the previous displacement and previous predicted displacement Example

• Previous motion vector is (1,0), i.e. one pixel to the right

• The predicted center of search window can be the position next to the center of the previous block

Search Window

Block

Center of Search Window

Adaptive Search Window

MotivationTo Increase the speed of fast full search algorithm by searching the most probably optimal position first

• Need to corporate with Spiral Scan

To increase the chance of finding the true optimum point

• Explained in the following slides

Conventional Search WindowWe used web camera to track the motion of an object and graph showing its x-axis velocity against time is plotted

Due to the limited size of search window, if an object is moving too fast, the optimal position would fall out of the search window, detection error results

|Velocity| < W pixels/sAssume the algorithm is run every second

Adaptive Search WindowBased on the previous optimal position and motion vector, we estimate the next optimal position, and this will be the center of the search window

P: Predicted Displacement P’: Previous Predicted DisplacementL: Learning Factor, range is [0.5, 1.0]D: Previous Displacement

DLPLP

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Adaptive Search WindowApplying adaptive search window method, the relative velocity fall within the range [-20,20], all true optimum points fall into the search window and thus no serious detection error results

Relative velocity = actual disp. – expected disp.

|Acceleration (relative velocity)| < W pixels/s

W: Search RangeAssume the algorithm is run every second

Raster Scan MethodConventional Block Scanning Method

when we use the previous block to find a best match in the current frame, we calculate the Sum of Absolute Difference (SAD) of the previous block with the current block at the left top position of the search window first. Then scan from top to bottom, from left to right.

Simply to implement

Small code overhead1 2 3 4

5 6 7 8

9 10 11 12

13

14 15 16

Search Window

# represents thepriority of each current block in block matching

Spiral Scan MethodProposed Block Scanning Method

Observation• The order of scanning can affect the time to reach the

optimum candidate block • When SEA, PPNM or PDE method are used, this can affect

the amount of computation

• When adaptive search window method is used, the motion vectors are center biased

Objective• Search the motion vector around the center of a search

window first• Higher chance to meet the optimal position earlier

algorithm run faster

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Spiral Scan MethodFirst find the SAD at the center of the search windowThen find the SAD at position that are n pixels away from the center where n = [1,BW]

Search Window

Spiral Scan MethodProposed Block Scanning Method

Result• Require larger memory space

– If fast calculation of block sum is used together, the whole block sum 2D array is needed to be stored.

• A bit larger code overhead– Degradation in speed not significant

– Spiral Scan Complexity: O(DX2)

– SAD Complexity: O(DX2 BW2)

• Speed of Algorithm significantly improved, when use with adaptive search method, about 2-3 times speed-up in real-time motion tracking

Table showing the time required to find the motion vector at different regions using different algorithms (Each algorithm is run 5 times using 2.0GHz CPU)Result

Time

Typical Point

ESA

SAD

Algorithm

ESA

PDE

SAD

Algorithm

SEA

PPNM

SAD

Algorithm

SEA

PPNM

PDE

SAD

Algorithm

Spiral

SEA

PPNM

PDE

SAD Algorithm

Adaptive

Spiral

SEA

PPNM

PDE

SAD

Algorithm

High Gradient

High Variance 2078ms 484ms 156ms 109ms 16ms 16ms

Low Gradient

High Variance2203ms 515ms 578ms 375ms 94ms 47ms

Low Gradient

Low Variance2078ms 360ms 359ms 203ms 79ms 47ms

Optimal Motion Vector = (-5, 12), Previous Motion Vector = (-2, 4) AffectingSpiral Scan and Adaptive Search Window algorithm

Static Frame Motion Tracking

This is just to illustrate speed is possible to be improved with our final

algorithm, a more general measurement will be

presented at the next slide

Algorithm using adaptive spiral method can reduce the average distance between search window’s center and optimum block’s position, thus improve the speed of algorithm ( as illustrated in the previous slide )

Result

Real-Time Motion Tracking

Average Velocity/Distance ~ 15 pixels Average Velocity/Distance ~ 6 pixels

Summary Video Source captured by camera Extractor Source Frames

Already selected feature block?

Feature Selection

delayBlock-matching Algorithm using two image frames

Transmitter

Server Application

Frame tYes

No

Frame t-1

A feature block is selected as reference block

MV of reference block

e.g. Bluetooth

Contribution

Proposed a method to improve the block matching algorithm for our application

Adaptive Spiral Method• Improve performance• Require larger memory space

New combination• Adaptive Spiral SEA PPNM PDE SAD

algorithm

Testing Platform on Window In order to test the performance of our algorithms, we have written a GUI program using Window MFC and OpenCV library.

Testing Platform on Symbian We finally built an application on Symbian as an testing platform to further test and fine tune our algorithm. Ready for other applications to build on top and use the motion tracking result directly.

Simple Applications finishedA “pong” game written in C#

Play in Window using Web camera as input device

A “pong” game written in Symbian Language

Play in Symbian phone using onboard camera

Future Work

Further improve the block matching algorithm by hierarchical method

Study and implement algorithms to detect rotation angle

Develop virtual mouse application

Develop multiplayer game

Build motion tracking API on Symbian

Q & A