Object Detection

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Another way of doing object detection and to reduce this tedious work is by combining the previous two task into one network. Here, instead of proposing regions for every images the model is fed with a set of pre-defined boxes to look for objects. So prior to the training phase of a neural network some pre-defined rectangular boxes that represents some objects are given to the network to train with. So when a image is gone through the network

Here pre-trained yolo-v3 has been used, which can detect 80 different objects. Although this model is faster but it doesn’t give the reliability of predicting the actual object in a given frame/image. It’s a kind of trade-off between accuracy and precision.

From the initial part, we understood that to measure the distance from an image, we had to localize it first to get the depth information. Now, how localization works?

As we know, an image goes refracted when it goes through a lens because the ray of light can also enter the lens, whereas, in the case of a mirror, the light can be reflected. That’s why we get an exact reflection of the image. But in the case of the lens image gets a little stretched. The following image illustrates how the image and the corresponding angles look when it enters through a lens.

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