  Object detection serves as a significant step in improving performance of
complex downstream computer vision tasks. It has been extensively studied for
many years now and current state-of-the-art 2D object detection techniques
proffer superlative results even in complex images. In this chapter, we discuss
the geometry-based pioneering works in object detection, followed by the recent
breakthroughs that employ deep learning. Some of these use a monolithic
architecture that takes a RGB image as input and passes it to a feed-forward
ConvNet or vision Transformer. These methods, thereby predict class-probability
and bounding-box coordinates, all in a single unified pipeline. Two-stage
architectures on the other hand, first generate region proposals and then feed
it to a CNN to extract features and predict object category and bounding-box.
We also elaborate upon the applications of object detection in video event
recognition, to achieve better fine-grained video classification performance.
Further, we highlight recent datasets for 2D object detection both in images
and videos, and present a comparative performance summary of various
state-of-the-art object detection techniques.
