  In a self-driving car, objection detection, object classification, lane
detection and object tracking are considered to be the crucial modules. In
recent times, using the real time video one wants to narrate the scene captured
by the camera fitted in our vehicle. To effectively implement this task, deep
learning techniques and automatic video annotation tools are widely used. In
the present paper, we compare the various techniques that are available for
each module and choose the best algorithm among them by using appropriate
metrics. For object detection, YOLO and Retinanet-50 are considered and the
best one is chosen based on mean Average Precision (mAP). For object
classification, we consider VGG-19 and Resnet-50 and select the best algorithm
based on low error rate and good accuracy. For lane detection, Udacity's
'Finding Lane Line' and deep learning based LaneNet algorithms are compared and
the best one that can accurately identify the given lane is chosen for
implementation. As far as object tracking is concerned, we compare Udacity's
'Object Detection and Tracking' algorithm and deep learning based Deep Sort
algorithm. Based on the accuracy of tracking the same object in many frames and
predicting the movement of objects, the best algorithm is chosen. Our automatic
video annotation tool is found to be 83% accurate when compared with a human
annotator. We considered a video with 530 frames each of resolution 1035 x 1800
pixels. At an average each frame had about 15 objects. Our annotation tool
consumed 43 minutes in a CPU based system and 2.58 minutes in a mid-level GPU
based system to process all four modules. But the same video took nearly 3060
minutes for one human annotator to narrate the scene in the given video. Thus
we claim that our proposed automatic video annotation tool is reasonably fast
(about 1200 times in a GPU system) and accurate.
