Background:
Object detection algorithm scans every frame in the video to detect the objects present which is time consuming. This process becomes undesirable while dealing with real time system, which needs to act with in a predefined time constraint. To have quick response we need reliable detection and recognition for objects.
Methods:
To deal with the above problem a hybrid method is being implemented. This hybrid method combines three important algorithms to reduce scanning task for every frame. Recursive Density Estimation (RDE) algorithm decides which frame need to be scanned. You Look at Once (YOLO) algorithm does the detection and recognition in the selected frame. Detected objects are being tracked through Speed-up Robust Feature (SURF) algorithm to track the objects in subsequent frames.
Results:
Through the experimental study, we demonstrate that hybrid algorithm is more efficient compared to two different algorithm of same level. The algorithm is having high accuracy and low time latency (which is necessary for real time processing).
Conclusion:
The hybrid algorithm is able to detect with a minimum accuracy of 97 percent for all the conducted experiments and time lag experienced is also negligible, which makes it considerably efficient for real time application.
Real-Time Object Detection and Recognition on Low-Compute Humanoid Robots using Deep Learning
