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Taking Motion Analytics To a New Level With AI



The common bottlenecks which might slow down your business growth

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Partial Occlusion

Inability to remove partial occlusion, clutter, noise, and object fragmentation from a scene.

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Weak Analytics

Weak and inaccurate motion analysis to identify the object movements without a hassle.

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Object Segmentation

Inability to segment the secondary object's from the primary object whose motion needs to be analyzed.

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Pattern Analysis

Weak pattern analysis to identify and measure the possible pattern of a moving object.

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Background Subtraction

Inability to locate the object whose motion needs to be detected by separating it from the background.

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Time-motion Co-relation

Inability to correlate the accurate time required for an object's movement to its actual motion.

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AI in Motion Analytics To Unlock Movement Intelligence

Powering motion analytics with AI to explore the hidden insights of object movements.


Edge Detection

Leveraging artificial intelligence in motion analytics includes learning the edge detection of the objects. Edges are critical local changes of intensity in an image. Edges usually occur on the boundary between two separate regions in an image. The edge detection in motion analytics AI aims to produce something like a line drawing of an image. In practice, we will look for parts in the image where the intensity changes quickly. Different objects are usually of different colors or hues, and this causes the image intensity to change as we move from one object to another. In addition, different surfaces of a single object receive different amounts of light, which again creates intensity changes. Our motion analytics AI software solutions leverage the Prewitt operator edge detection method. This method detects two types of edges in an image, they are horizontal edges and vertical edges. The motion analytics AI software calculates the edges upon the basis of the difference between corresponding pixel intensities of an image.

Background Subtraction

Motion analytics AI software includes background subtraction. It is a technique in image processing and computer vision where an image’s foreground is extracted for additional processing for object recognition. The customized motion analytics AI solutions process the image for image denoising and object localization top locate the object whose motion is needed to be detected. To detect moving objects in videos from the static cameras, we leverage background subtraction in motion analytics AI. Our approach is to detect the moving objects from the difference between the current image frame and an initial reference frame (background image/background model). Our motion analytics AI software solutions offer advanced background subtraction to analyze and detect the human motion in a video sequence. Artificial intelligence motion analytics is used in many emerging video applications, like video surveillance, gesture recognition, and traffic monitoring.

Object Motion Tracking

OSP leverages AI in motion analytics for a real-time object tracking by using the edge as a feature. The edge detection in motion analytics AI helps to detect the object’s motion using scene illumination variation, full and partial occlusion of the moving object and object shape deformation. The Prewitt operator can be utilized for object detection. After detecting the edge of the object, our motion analytics AI carry out object tracking through the Kalman filter. In an object tracking scenario, whatever may be an object it can be a person walking on a track, helicopter in the air, vehicle on the road or a boat in the sea. We customize and train the motion analytics AI model for object representation by its shape, appearance, and application.

Scene Analysis

In OSP’ tailored motion analytics AI software, the scene analysis is broadly a primary subset of knowledge parsing. The motion analytics AI incorporates advanced scene analysis system, which involves the segmentation of a scene into a set of coherent patterns and the recognition of memorized ones. For an example, in the process of analyzing a riven bank scene, high-level feature alignments are recorded of things which are usually observed at a riven bank like stones, trees, small bridge, water, sky, etc. While trying to capture the motion of a specific object like a boat with our custom-made motion analytics AI software solutions partial occlusion and variability of loacations in the scene are not the issues. Our tailored artificial intelligence motion analytics is well equipped with handling errors, omissions, noise, commissions and the substitutions. OSP’ motion analytics AI can handle partial visibility of the objects and recognize them successfully in the consequent frames.

Time and Measurement Analysis

In the case of Kalman filters, each iteration begins with predicting the process’s state using a linear dynamics model. For state prediction of the selected object, our custom motion analytics AI software considers the state is a 4-dimensional vector where the coordinates of the object’s center and its velocity is measured. We leverage artificial intelligence motion analytics for error covariance prediction. The Kalman filter concludes the time update steps by estimating the error covariance forward by one time step at a time. Our motion analytics AI software solution can provide optimal estimation in linear and nonlinear tracking scenarios. The input images are divided into disjointed blocks with the corresponding fixed size. Then the TSS algorithm in artificial intelligence motion analytics searches for the best match block with the least sum of absolute difference values.

Motion Pattern Analysis

OSP’ motion analytics AI solutions has the potential to evaluate the movement direction of objects within the video. Our customized motion analytics AI leverages optical flow and mixture of Gaussian feature to analyze the pattern of the object’s motion. Three main parameters highly influence the success rate of this model, they are frame interval, grid size, and some frames analyzed. Understanding the pattern of motion using motion analytics AI software solutions has its wide applications in the riots detection and abnormal movement in public places. The motion analytics AI can also help in wearables, where attached to a person’s hand while walking to extract information about their walking cycles.

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Use Cases

What We Offer?

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Moving Object Detection

OSP' customized motion analytics AI can accurately detect the moving objects using repurposing classifiers or localizers. We apply the model to an object at multiple locations and scales to find the high scoring regions of the image. We apply a single neural network to the full set of images. This network segments the image into multiple regions and predicts bounding boxes and odds for a specific region. These predicted probabilities offer the near accurate measurements for moving objects.

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Motion Intelligence

Our customized motion analytics AI solutions detect and recognize the object of your interest in video frames accurately. It is made possible with multilevel hierarchical sections and subsections, based on the polythetic categories. OSP' team can custom-build motion analytics AI to track the recognized objects using the species-based Particle Swarm Optimization (PSO). The artificial intelligence motion analytics use the polythetic concepts and pattern analysis to eliminate the redundant noise, handle occlusion, and discover natural structures.

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Gesture Recognition

Making the human motion analysis with AI requires the gesture recognition and gesture authentication to understand the human actions from an egocentric video or a third person view. The gesture analysis from the motion analytics AI solutions requires target application, nature of gestures, nature of inputs, modalities used for motion analysis, and spacial strategy. Our gesture recognition module in motion analytics AI works on pre-processing operations, constructing or leaning data, and matching, regression or classification to provide the gesture & action recognition and pose reconstruction.

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