LocNet: Improving Localization Accuracy for Object Detection

Understand

We propose a novel object localization methodology with the purpose of boosting the localization accuracy of state-of-the-art object detection systems.

• Our model, given a search region, aims at returning the bounding box of an object of interest inside this region.
• To accomplish its goal, it relies on assigning conditional probabilities to each row and column of this region, where these probabilities provide useful information regarding the location of the boundaries of the object inside the search region and allow the accurate inference of the object bounding box under a simple probabilistic framework.
• For implementing our localization model, we make use of a convolutional neural network architecture that is properly adapted for this task, called LocNet.