Real time traffic tracking using matching algorithms
In this project, the students are expected to perform the "matching algorithms" to capture moving pedestrians and vehicles traffic and compare them quantitatively.
Computer Science · Course
Basics of Computer Vision using Python
A comprehensive course on Computer Vision using Python. This course is highly suited for beginners
- Duration
- 12 weeks
- Content
- 21 hours
- Projects
- 2
- Mode
- Online
OverviewWhat you'll
What you'll
be able to do.
- This course provides the parameters and processes that define computer vision and delve into details like image processing, edge detection and image segmentation.
- This course provides the basics of classical computer vision, 2d and 3d processing, camera projections, use of machine learning in vision systems
Tools you'll use
PythonOpenCVTensorFlowKeras TensorFlow 2
Curriculum12 modules,
12 modules,
built with industry.
01Basics of Computer Vision using Python
- Week - 01 Introduction to Computer VisionW01
- General introduction,
- History of CV
- Formulating the field, why is it a hard topic?
- Definition of computer vision
- Required components
- What qualifies as a vision system
- Humans as a vision system: how good do we “see”?
- Useful applications
- Image acquisition using a camera
- Different types of cameras for different domain
- Stills, Video, DSLR, Bodycam, Drone
- Infrared, Ultrasonography, Magnetic resonance
- Physics of Color: color spectrum
- Human encoding of color: Rods and cons of eyes
- Color spaces:
- RGB, CMYK, HSV
- Color balance
- Camera specifications:
- Pinhole
- CMOS
- CCD
- Image specifications:
- Pixel (Picture element)
- Aspect ratio, HD, Interlacing
- Conversions
- Type of digital images:
- Binary, Grayscale, Color
- Conversion techniques
- Week - 02 Image processingW02
- Noise Removal
- Pixel Neighborhood
- Salt and pepper noise
- Morphing to hide cracks in the image
- Applying filters to images
- Convolution of matrices
- Types of Filter:
- Mean or Box filtering
- Median Filter
- Mode, Mean, Pass
- Generic properties of smoothing
- Anisotropic filtering
- Gaussian: Isotropy condition, formulation, figure
- Weight influence of pixels by their distance to the center pixel
- Spread parameter
- Motivating examples
- Filter Separability
- Computation and Maths
- Gaussian Separability
- Week - 03 Edge DetectionW03
- Introduction to edges and gradients
- Intensity difference
- 1D versus 2D edge detection
- Edge detection in mammals
- 1D signals and 2D signals
- Difference and derivative mask
- Examples
- Image Gradient
- Image noise: Gaussian noise
- Smoothing + Edge detection
- Gaussian Derivative Signals
- 2D gradient operators
- Prewitt Masks
- Sobel Masks
- Steerable filters
- Laplacian filters
- Laplacian of Gaussian
- Zero Crossings
- Canny edge detection
- Hysteresis thresholding
- Non-maximal Suppression
- Week - 04 Image Segmentation and featuresW04
- Thresholding based on histogram
- Otsu, Adaptive Otsu
- Formulation, Advancements, and effectiveness
- Examples
- Distance Metric: Norm functions
- Thresholding based on different metrics, covariance-based
- Different types of background subtraction
- Mean, Euclidean, Mahalanobis
- Covariance matrix, multidimensional mahalanobis
- Shadow modeling
- Transform to color spaces
- Multimodal background distribution
- Gaussian Mixture Model
- Foreground Assignment
- Clustering to Image Segmentation
- Agglomerative Clustering
- K Means, K Means ++
- Mean Shift Clustering
- Hierarchical Clustering
- Week - 05 Binary Image OperationW05
- Morphology:
- Erosion, Dilation
- Open, Close
- Connected component
- Counting objects: Sequential count etc
- Recursive count
- Remove Small Features
- Hough Transformation Algorithm
- Radon Transformation Algorithm
- Image Pyramids: Gaussian Laplacian Coding Compression
- Week - 06 Shape of ObjectsW06
- Largest component
- Medial axis
- Boundary coding
- Chain Coding
- Shape Numbering
- Quadtree Representation
- Bounding box
- Perimeter, Compactness, Circularity
- Centroid
- Spatial Moments
- Central
- Second third order
- Similitude Moment
- Dimensionality Reduction
- Linear basis set
- Principal Component Analysis
- Eigen Values and Vectors
- Finding Eigen sets
- Test on synthetic and real data
- Face Recognition using PCA: kernel trick
- Week - 07 MotionW07
- Definition, simple motion
- Image differentiation
- Single constant threshold
- Weber's Law
- Optical flow
- Formulas, geometry, example
- Normal optic flow
- Weighted aggregate,
- Hierarchical Motion Estimation
- Motion: Use of linear Algebra
- 3D motion of a point
- Matrix operations for different motion in objects
- Pinhole Camera Model
- 2D matrix motion
- Translation Motion
- Similarity Motion
- Affine Motion
- Motion History Image
- Spatial Pattern of where motion occurred
- Progression of motion
- Motion Energy Image
- Silhouette Difference
- Week - 08 Matching & TrackingW08
- Motivation, Example
- Feature-based tracking
- How to find good features to track
- Find Interest Points (General)
- Panoramic stitching
- Features from Accelerated Segment (FAST)
- Harris Detector
- Gradients
- Window weighing function
- Harris Corner Response Function
- Week - 09 Interest PointW09
- SURF algorithm
- SIFT algorithm for automated feature selection
- Free alternative to SIFT and SURF in OpenCV
- Laplacian Of Gaussian
- Automated Feature selection
- Diff Of Gaussian
- Covariance tracking
- Descriptor Matrix
- Finding best match
- Rotation Invariance
- Kanade-Lucas-Tomasi (KLT) Tracker
- Tracking Features
- Formulations
- Reduction Pyramid
- Select “good” feature based on Eigen Value
- Mean shift tracking
- Weighted histograms using spatial kernels
- Evaluating similarity between distributions using Bhattacharyya coefficient
- Object tracking by target localization (in each frame) by maximizing the similarity function using mean shift
- Template Matching
- Sum-of-Absolute Differences
- Sum-of-Squared Differences
- Normalized Cross-Correlation
- Week - 10 Image RegistrationW10
- Lens
- Thin Lens Model
- Focus, DoF, Aperture,
- Projective Camera Model
- Pinhole Camera
- Intrinsic and Extrinsic Camera Parameters
- Homogeneous Coordinate
- Projection
- Camera Projection
- Camera Matrices
- Estimating camera matrices
- Extracting parameter P
- Calibration
- Projection
- Perspective Effective
- Affine
- Orthographic
- Weak
- Transformation: Translation, Rotation, Skew, Reflection
- Planar homography/ Projective Transformation
- Solving homography matrix
- Normalized Direct Linear Transformation
- Example on real 3D data
- RANSAC Algorithm
- Gold Standard Algorithm
- Week - 11 Lens & Camera projectionW11
- 3D intro
- Motivation
- Ambiguity in single View
- Geometry for simple stereo system
- Depth and Calibration
- Epipolar Geometry
- Baseline, Epipole, Epipolar Line, Epipolar Plane
- Epipolar Constraint
- Converging camera
- Parallel Camera
- Camera Motion
- Fundamental Matrix
- Computation
- 8 point algorithm
- Depth Matrix
- Stereo Matching Algorithm
- Correspondence Search
- Estimate disparity by finding corresponding points
- Depth is inversely related to disparity
- Stereo Matching as Energy Minimization
- Graph cut algorithm
- Week - 12 SOTA ML based CV TechniquesW12
- LeNet
- AlexNet
- General detection techniques
- YOLO
- GAN
- Autonomous Vehicle Specific Networks
- Gaussian Neural Network
- Confidence in classification output: decide object confidence for autonomous vehicle
- Week - 01 Introduction to Computer VisionW01
Industry projects2 projects
2 projects
you'll ship.
Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 2 Projects that are available in this program.
Real time video segmentation using machine learning and feature extraction
In this project, students will have to download public road video dataset and design a machine learning based technique to segment and identify specific tasks related to autonomous…
FeeOne fee.
One fee.
Everything included.
Become job ready with our comprehensive industry focused curriculum for freshers & early career professionals
Premium
₹40,000
Inclusive of all charges- 1 Year Access
- Personalized Page
- Live Individual & Group Sessions
- Personalized & Hands-On
- Job-Oriented Industry Relevant Curriculum
FAQQuestions,
Questions,
answered.
Is Machine Learning an important part of Computer Vision?
Computer vision involves various algorithms, and hence Machine Learning has become an important part of it.
Will we be provided with some reading material for the computer vision course?
Yes, a lot of reading material is provided for you to read from and practice.
Will Cloud Services be a part of the computer vision course?
Yes, but you are not required to be a specialist in the same. An introductory knowledge would be more than enough.
Will I get a certificate post the completion of the course?
You will get a certificate of completion once you finish the course.
Is the course relevant for engineers?
Students who come from a mechanical and computer science background can enrol in this course.
Will the project involve the elements of Image Processing?
Yes, image processing forms a very important part of the live project.
Can I take a trial session before paying for the course?
Yes, you can book a one-on-one demo before making the payment for the course.
Can Computer Vision be used in Autonomous Vehicles?
Computer Vision is very actively used in Autonomous Vehicles, and you can take up the course if you are developing new-age vehicles.
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the right fit?
A counsellor will walk you through the syllabus, projects and fee options — and tell you honestly if another course suits you better.