A REAL TIME OBJECT DETECTION SYSTEM
Abstract
This system presents a method for detecting objects in images using a single deep neural network.
Our approach, is a combination of the Faster Region-base Convolutional neural network and the single shot detection. The system discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments. to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. SSD is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stages and encapsulates all computation in a single network.
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