Open source computer vision project for image captioning

Have you ever wished for some technology to subtitle your social media images because neither you nor your friends could come up with cool captions? Deep learning for image captions is here to help.

Image captioning is the process of generating a text description for an image. It is a combined task of computer vision and natural language processing (NLP).

Computer vision methods help to understand and extract features from input images. In addition, NLP converts images into text descriptions in the correct order of words.

Here are some useful data sets to help you use image captions:

1.COCO Caption

COCO is a large-scale object detection, segmentation and captioning data set. It consists of 3.3 million images (labeled > 200K), with 1.5 million object instances and 80 object categories, with five headings for each image.

2.Flicker 8k dataset

It is an image captions corpus consisting of 158,915 crowdsourced captions describing 31,783 images. This is an extension to the Flickr 8K dataset. New images and titles focus on people going about everyday activities and events.

Open source computer vision project for human posture estimation

Human posture estimation is an interesting application of computer vision. You’ve heard of Posenet, an open source model for human posture estimation. In short, pose estimation is a computer vision technique that can infer the pose of a person or object present in an image/video.

Before discussing how postural estimation works, let’s first look at the “human postural skeleton.” It’s a set of coordinates that define a person’s posture. One pair of coordinates is the limb. In addition, pose estimation is performed by identifying, locating, and tracking key points in the human pose skeleton in images or videos.

To develop a pose estimation model, here are some data sets:

1.MPII

The MPII Human Pose dataset is the latest benchmark for evaluating joint Human Pose estimation. The data set contains about 25K images, which contain more than 40,000 people with annotated human joints. Overall, the data set covers 410 human activities, and each image has an activity tag.

2.HUMANEVA

The HumanEVA-I dataset contains seven calibrated video sequences synchronized with 3D human postures. The database contains four objects that perform six common actions (e.g., walking, jogging, gestural gestures, etc.), which are divided into training, validation, and test sets.

I found Google’s DeepPose is a very interesting research paper, which uses deep learning models to estimate poses. In addition, you can access multiple research papers on pose estimation to better understand it.

Open source computer vision project for emotion recognition through facial expressions

Facial expressions play a crucial role in nonverbal communication and in recognizing people. They are important for identifying human emotions. As a result, information about facial expressions is often used in automated systems for emotion recognition.

Emotion recognition is a challenging task because emotions can vary depending on environment, appearance, culture, and facial response, leading to ambiguous data.

Facial expression recognition system is a multi-stage process, including facial image processing, feature extraction and classification.

Here’s a data set you can practice on:

Real-world Affective Faces Database

The Real-World Affective Faces Database (RAF-DB) is a large-scale facial expression Database containing approximately 30K diverse facial images. It consists of 29,672 real-world images and a 7-dimensional representation distribution vector for each image.

Open source computer vision project for semantic segmentation

Semantic segmentation comes into play when we talk about complete scene understanding in computer vision technology. The task is to classify all the pixels in the image into related categories for the object.

Open source computer vision project – Semantic segmentation

Here is a list of open source datasets that practice this topic:

1.CamVid

This database is one of the first semantically broken data sets to be published. This is often used in (real-time) semantic segmentation studies. The data set contains:

367 for training

101 verification pairs

233 test pairs

2.Cityscapes

The dataset is a processed subsample of the original urban landscape. The data set has a still image of the original video, and semantic segmentation labels are displayed in the image next to the original image. This is one of the best data sets for semantic segmentation tasks. It has 2975 training image files and 500 verification image files, each image is 256×512 pixels.

Open source computer vision project for road lane detection for autonomous vehicles

A self-driving car is a vehicle that can sense its environment and operate without human participation. They create and maintain a map of their surroundings based on various sensors installed in different parts of the vehicle.

These vehicles have radar sensors that monitor the location of nearby vehicles. Cameras detect traffic lights, read road signs, track other vehicles and lidar (light detection and ranging) sensors reflect pulses of light from around cars to measure distance, detect road edges and identify lane markings.

Lane detection is an important part of these vehicles. In road transport, a lane is a section of a carriageway designated for single-lane vehicles to control and guide drivers and reduce traffic conflicts. Here are some data sets available for the experiment:

1.TUsimple

The data set is part of the Tusimple Lane Detection Challenge. It contains 3,626 video clips, each one second long. Each of these video clips contains 20 frames, with an annotated final frame. It contains the training and test data set, which contains 3626 video clips, 3626 annotated frames in the training data set and 2782 video clips for testing.

In fact, there have been more and more AI based computer vision projects began to be used in practical scenarios. For example, support face recognition and license plate recognition of EasyCVR video intelligent analysis platform, based on AI intelligent recognition and multi-target tracking technology, comprehensive processing and analysis of video images from road surveillance cameras, can sense a lot of key information.

Using deep learning technology, can realize traffic violation analysis judgment, can undertake recognition of face, vehicles, real-time monitoring analysis of road traffic, in violation of license plate images, driver fatigue state, AI can be in transportation scheduling, transportation planning, traffic management and traffic safety prevention behavior play an important role in the scene.