Speed calculation using Image differences

    6 Votes

Speed of the moving object can be calculated from the images taken from it. The most important phase in this is the detection of the repeating object in the subsequent images. Object tracking is done by Lucas-Kanade Algorithm, which is widely used differential method. By combining information from several nearby pixels, the Lucas-Kanade Algorithm can often resolve the ambiguities of the optical flow. The Lucas-Kanade Algorithm is implemented in the using OpenCV, a library of programming functions mainly aimed at real time computer vision. From the tracked object from the subsequent images the pixel difference is calculated. This measurement converted into meter. With the known value of the time interval between the subsequent images the velocity is calculated.

System Design

This project can be split into four phases. They are

  • Detection of Objects
  • Tracking of Objects
  • Calculating Speed
  • Capturing Object's Picture

Success of this project depends upon the accurate detection of moving object in the video stream. But object detection is a very difficult task. At first, Speed Detection Camera System (SDCS) will segregate video streams into moving and background components. By detecting moving blobs, recognition and analysis of objects in the frame becomes more efficient. In this project, we try to establish a similarity between objects or object parts in consecutive frames. Speed, trajectory and direction of the objects also can be extracted from the frame. By tracking objects, temporal information about objects are extracted and high level behavior analysis is conducted. 

Object Detection

By using adaptive background subtraction and three frame difference algorithm moving objects are detected. Here stationary objects when moves, a hole is made in place of original position. Motion Matrix, masked subtraction and Generation of new background Threshold matrix is used to overcome the problems created by holes in background subtraction.

Objects Tracking

Object segmentation, labeling and Object center extraction are three phases involved in object tracking. In segmentation, we assume that the objects are connected as one part. Labeling of the detected object in the frame from the moment it enters the scene to the moment it leaves constitutes the second part. Next part is finding the center of the object.

Speed Calculation

By recording the frame number of object entering frame and object leaving frame, we can find the number of frames in between those two. Since we know the duration of each frame, speed of the object can be easily calculated.

Capturing Object's Picture

To capture an object with good resolution in a picture, wait for the object to be at the center of the scene.

Popular Videos


How to improve your Interview, Salary Negotiation, Communication & Presentation Skills.

Got a tip or Question?
Let us know

Related Articles

Travel Planner using Genetic Algorithm
Data Recovery and Undeletion using RecoverE2
Routino Router Algorithm
Data Leakage Detection
Scene Animation System Project
Data Structures and Algorithms Visualization Tool
Paint Program in C
Solving 0-1 Knapsack Problem using Genetic Algorithm
Software Watermarking Project
Android Gesture Recognition
Internet working between OSI and TCP/IP Network Managements with Security Features Requirements
Web Image Searching Engine Using SIFT Algorithm
Remote Wireless Sensor Networks for Water Quality Monitoring Requirements
Ranking Spatial Data by Quality Preferences
Scalable Learning Of Collective Behaviour
Computational Metaphor Extraction And Interpretation
Designing a domain independent Rules Engine For Business Intelligence
Graph Colouring Algorithm
Gesture Based Computing