2D Bounding Box

2D Bounding box annotation service is required for precise object detection like cars, people, goods etc to train AL/ML models. Intellekt through its advanced Annotation platform can quickly create bounding box and replicate this across vast set of images which are required for training the model with very high level of accuracy for industries like Self Driving Cars, Image Tagging for Retail, developing the object recognition perception models.

Polygon/Contour Annotation

Polygon annotation is required to detect irregular shaped images or coarse objects in videos to observe the exact shape of object which can then be used in the AI models. It is suitable for objects detection in medical images, road signs, various actions of sports persons or other objects of interest with best accuracy.

Semantic Segmentation

The goal of semantic segmentation is to label each pixel of an image with an associated class making computer vision to localize the images with dense prediction. Our tool can visualize multiple objects of the same class as a single entity such as car, building, window. All the pixels belonging to the same class can be given a particular color and then can be used for the perception model training.

Cuboidal Annotation

Cubodial Annotation is required for to annotate 3D objects from images and videos so that once can detect exact dimension of objects like movement for self-driving cars. Our 3D cuboid annotation helps autonomous driving models to comprehend the real-world scenario. In such models cars can sense the distance of each object from itself and also measure the spacing.

Polyline Annotation

Polylines annotation is required for lane detection in autonomous driving cars and requires to define directions, lanes, road divisions, directions, and opposite direction traffic to be able to detect the surroundings for safe and trouble-free driving. Intellekt can automate polyline annotation services with very high level of accuracy and help build and train the AI models at scale and quickly.

Bounding Boxes and Object Tracking

Intellekt’s object tracking completely changes the game. Now, an entire video sequence can be assessed as a whole as against the traditional approach to split the video into individual images and then annotate each image separately, paying attention to make sure that there are consistent identifiers for each unique object in continuous images.

This feature makes it much easier and faster to follow a single object -- even if it's moving -- from beginning to end of a video. If the object does not appear in sequential images from the camera view and reenters later, we can easily and accurately accommodate it. As the density of the objects increase, using our sophisticated tools, we can make the whole process more efficient while maintaining the highest annotation quality.

LiDAR 3D Point Cloud annotation

LiDARs is one the most essential sensor used by autonomous vehicles companies, operating at L4-L5 levels of autonomy. Usually deep learning algorithms need a huge amount of training data and annotating this data through LiDAR point cloud is a challenging task due to complex annotation process, low resolution, and it’s very time-consuming. But Intellekt’s team performs this job perfectly with expertise in image annotation services to create training data for machine learning algorithms.

3D boxes help detect the objects more precisely and tracks the single points with excellence to gather the critical details like location, size, yaw, speed, pitch with class. Intellekt’s data annotation team uses our advanced inhouse tool to label different types of objects including objects of interest, pedestrians, drivable lanes etc.

3D Cuboid Annotation Services

3D cuboid annotation is usually used to train Robotics in several industries like automotive, satellite imagery used for defense, warehousing, etc. which helps better perception models that work continuously without any human interference. The images captured from 2D cameras can be annotated with 3D cuboid annotation and put in a model making it perceptible for robots and drones imagery used in various fields. Our 3D cuboid images assist in training the computer vision model by providing better in-depth object detection. Indoor objects like couch and other furniture can be annotated with superior quality and precision of pixels.

Lidar Semantic Segmentation

3D Lidar Datasets are not available in large quantities and are extremely expensive to label. Applications like Autonomous Driving algorithm will only be successful if it has the semantic understanding of the three-dimensional world around them. State of the art methods use deep neural networks to predict semantic classes for each point in a LiDAR scan.. Intellekt performs a comprehensive experimental study of image-based semantic segmentation architectures for LiDAR point clouds. We demonstrate various techniques to boost the performance of processing and also improve runtime as well as memory constraints

Audio Transcription

Audio transcription converts spoken language into written language and is used in wide range of industries, including e-commerce, legal, medical, and technology. Through our experts we provide superior Audio transcription services along with add-on services such as time stamping multilingual audio, support for different file types and speaker identification.

Audio Labeling and Linguistic Annotation

Labeling the audio sound is an important task, as it can help build models that enable the machines to process the sound in the audio. Also, for training the AI models and giving accurate results it’s important to understand or learn such audio when used in real-life. For audio labeling and linguistic annotation, Intellekt works with well-trained and highly experienced annotators, who can annotate all types of audio files with extra precision

Speech Annotation for NLP

The speech in an audio file consists of the different words and sentences. These words and phrases in the audio file are made recognizable to the machines through speech annotation. We, at Intellekt, carry out such kind of speech annotation using customized data labeling tools. In NLP, machine algorithms for recognizing speech requires linguistic annotation and we are capable to perform this annotation process.

Audio/Acoustic fingerprinting

Acoustic fingerprinting entitles a device to pick up as little as two seconds of a sound and then translates it into a code which is then matched against a preset code in an existing database to identify what sound is it. Buy this method it helps mitigate privacy concerns as the device really doesn’t share actual audio from the user. Shazam, the music app uses this method to identify a song on the radio.

LiDAR

Light Detection And Ranging (LiDAR) is a new device used by many self-driving vehicles to sense their surroundings. The output of a LiDAR device is a point cloud of the surroundings. For instance, when autos with LiDAR cameras drive around, the cameras collect 3D data in the form of light points.

We offer sensor fusion annotation for 3D point cloud, including LiDAR , radar, and camera. Annotation of the LiDAR point cloud involves identifying objects and drawing cuboid around the objects or identifying the object to which every LiDAR point belongs. This labeled data powers the algorithms required for autonomous vehicles, drones and maps.