Deep learning based tracker trough Multi-camera View
Object Detection
A deep learning-based approach
Based on the state-of-art detectors
Robust (trained on the dataset with noise, masking, corruption)
Acceptable Accurate
Support different type of Objects
Tested on different Object such as Pedestrians and Vehicles
Our detector needs optimization for processing time
Object Classifier
Based on SimpleNet and Efficient Net
Near Real-time
Accurate enough (about 95%)
Robust against camera changes, light conditions, weather conditions,etc
Multi-Object Tracker
Combining Tracking and Detection
Multi-object Tracker
Online learning (learning based)
Real-time tracker
CNN-based feature extractor and using Triplet-loss
Using Kalman filter for Motion Estimation
Scalability and Quality Assurance
The solution is highly scalable and can be used in several applications
It does not depend on the camera type, position or situation
The same solution can be applied in the similar scenarios
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