Real-Time Object Detection and Identification

Abstract

Object Detection is the process of finding real-world object instances like car, bike, TV, flowers, and humans in still images or Videos. It allows for the recognition, localization, and detection of multiple objects within an image which provides us with a much better understanding of an image as a whole. It is commonly used in applications such as image retrieval, security, surveillance, and advanced driver assistance systems (ADAS). Real time object detection is a vast, vibrant and complex area of computer vision. If there is a single object to be detected in an image, it is known as Image Localization and if there are multiple objects in an image, then it is Object Detection. This detects the semantic objects of a class in digital images and videos. The applications of real time object detection include tracking objects, video surveillance, pedestrian detection, people counting, self-driving cars, face detection, ball tracking in sports and many more. Convolution Neural Networks is a representative tool of Deep learning to detect objects using OpenCV (Open source Computer Vision), which is a library of programming functions mainly aimed at real-time computer vision. We also use Tensorflow object detection API and YOLOv3.

What is Object Detection

Object Detection is the process of finding real-world object instances like car, bike, TV, flowers, and humans in still images or Videos. It allows for the recognition, localization, and detection of multiple objects within an image which provides us with a much better understanding of an image as a whole. It is commonly used in applications such as image retrieval, security, surveillance, and advanced driver assistance systems (ADAS).

Object Detection can be done via multiple ways:

• Feature-Based Object Detection
• Viola Jones Object Detection
• SVM Classifications with HOG Features
• Deep Learning Object Detection

In this Object Detection Tutorial, we’ll focus on Deep Learning Object Detection as Tensorflow uses Deep Learning for computation.

Applications of Object Detection

Facial Recognition:

A deep learning facial recognition system called the “DeepFace” has been developed by a group of researchers in the Facebook, which identifies human faces in a digital image very effectively. Google uses its own facial recognition system in Google Photos, which automatically segregates all the photos based on the person in the image.

People Counting:

Object detection can be also used for people counting, it is used for analyzing store performance or crowd statistics during festivals. It is a very important application, as during crowd gathering this feature can be used for multiple purposes.

Self-Driving Cars:

Self-driving cars are the Future, there’s no doubt in that. But the working behind it is very tricky as it combines a variety of techniques to perceive their surroundings, including radar, laser light, GPS, and computer vision. Advanced control systems interpret sensory information to identify appropriate navigation paths, as well as obstacles and once the image sensor detects any sign of a living being in its path, it automatically stops. This happens at a very fast rate and is a big step towards Driverless Cars.

Security:

Object Detection plays a very important role in Security. It is also used by the government to access the security feed and match it with their existing database to find any criminals or to detect the robbers’ vehicle.