Vehicle-detection-tracking-and-classification using CNN

Vehicle detection, tracking and classification from video feed

Problem statement

The problem statement is to detect and identify vehicles in videos. Suppose you want to identify how many vehicles have passed in a lane during peak hours in a city. The purpose of doing this exercise might be multiple:

1. The government can use traffic flow data to decide the width of a new road in a nearby area.
2. The organisation who's building a highway can decide the toll rate based on the number of vehicles passing on a particular road.
3. The government often wants to ban certain types of vehicles (such as auto-rickshaws, trucks, etc.) based on the frequency of these vehicles on a particular road.

Broadly speaking, to achieve any of those tasks, there are two steps involved:

1. Vehicle detection: Here, you detect those vehicles which are moving on a road.
2. Vehicle classification: Here, you classify the detected vehicle into a particular class according to the application you're working on. For example, if you're interested in looking at the number of four-wheelers vs the number of two-wheelers, you'd classify each vehicle as a two-wheeler or a four-wheeler. Similarly, you can have classes such as auto-rickshaws, trucks, motorcycles, bicycles, etc. The exact classes need to be defined according to the problem statement.

Vehicle detection:

• The first step is to break a video into individual frames.
• Then, make an imaginary (virtual) line across the lane.
• To keep the track of the vehicles, increase the vehicle count by one whenever a vehicle crosses the imaginary line.

Vehicle classification:

• Crop the vehicle that just passed the imaginary line.
• Classify the cropped vehicle using a CNN classifier.

Data Source:

The recorded video file from the below source:
https://drive.google.com/file/d/1HxxhIf4dM7VILIb5-dR3qXDbeGPOLgv-/view