Wide variety of road types like intersections, highways poses a real challenge to the computer vision algorithms. Hence, there is a need of efficient algorithm to detect the accident on road and also evaluate the severity of the incident. This can be used to improve the emergency services response time. The work demonstrated in this paper aims to develop such an algorithm by modifying existing CCTV surveillance system. In this work, the accident is detected by the dispersion in the motion field of the vehicles during collision. Motion field of the road is obtained from the optical flow of the video frames. The moving objects in the frames are segmented and tracked. The dispersion in the angle vector of the optical flow is derived for each of the moving object. The dispersion of angle vector for each object is monitored, and deviation of the same from the threshold is determined as an accident. The harshness of the accident can be found by the range of dispersion of the motion field. The algorithm developed here is capable of detecting accidents between any types of moving objects.

Road Accident Detection and Severity Determination from CCTV Surveillance

The motivation behind this paper is to build up customized applications into easy to use Google Cloud Platform that contains 18000 pictures of butterflies from the Saturniidae, Nymphalidae and Papilionidae family to distinguish Butterfly classifications, image fruits or larval host plants and pupae deformities by incorporated trailblazing innovation to give profitable experiences into the activity. The exploration techniques utilized shading channels, mass channels, and shape discovery as exchange figured out the prepared pictures indicated using machine vision in CNN. The procedure will utilize a computer vision through scanning pupae quality before they are conveyed to the market of purchasers. The pictures were (a) transferred and named; (b) prepared, (c) assessed, and (d) tried in AutoML Vision. The computerized devices expectation certainty or accuracy normal is 97.1% and review of 91.3% out of 429 pictures 11 labels, 4 test images in top 5 labels appeared in the assessment. The next model was created and trained about 398 images9 labels, 4 test images. Precision and recall are based on a score threshold of 0.5. Precision help high exactness model produces less false positives 90.9% and review help high review model produces less false negatives 85.1%. The purpose of the study was to provide computerized instruments incorporated product search information communicate with GCP Cloud Vision API with the utilization of the Corvid Node Runtime to the online business website of Insects Connection, including machine vision, and sensor combination in items to robotize the retail exchange buy installment strategies.

Butterfly, Larvae and Pupae Defects Detection Using Convolutional Neural Network and Apriori Algorithm

Visual short-term tracking in a long sequence of images with a lot of pose variations, target deformations and different types of occlusion is one of the harshest tasks in image processing. Overcoming such challenges can be performed in a superior way by combining different trackers. In this paper, we propose a method that combines different algorithms for tracking the given target. The algorithms are KCF (Henriques et al. in IEEE Trans Pattern Anal Mach Intell 37(3):583–596, 2014), MIL (Babenko et al. in Visual tracking with online multiple instance learning. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, 2009), CSR-DCF (Lukezic et al. in Discriminative correlation filter with channel and spatial reliability. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017) and MOSSE (Bolme et al. in Visual object tracking using adaptive correlation filters. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2010) trackers. In this method, these algorithms can continuously correct each other’s faults. They also implicitly search for the target even when they are tracking it, to make sure the object they are tracking is the real target. Thus, they outperform many of the state-of-the-art trackers as well as their components when they work independently. In this paper, we examine three trackers which we made in such way. Two of these trackers run at speeds close to real-time on a CPU and so we compare them with some famous wide-spread tracking algorithms both in terms of accuracy and speed.

Auto-correct-integrated trackers with and without memory of first frames

India is one of the world’s most densely populated country, with more vehicles being used in urban areas. The ability to follow or track individual vehicles as well as public transport frameworks is useful. We can address complex challenges with effective results using computer vision and machine learning. The major purpose of our project is to deal with the concept of real-time-vehicle tracking is based on video stream by a surveillance camera to track vehicles. We would focus on the type of vehicle i.e., car or bike, Firstly, the software would slice the frames from the streaming video so that would be easier to detect the attributes precisely. Secondly, slice the area of the required part, for instance if we take the frame, the car in it would be cropped for focusing on the required output as correctly as possible. Lastly, the software would store the required data in the database for future usage. 

Traffic Congestion Management based on Vehicle Density Using Image Processing Techniques

India is one of the world’s most densely populated country, with more vehicles being used in urban areas. The ability to follow or track individual vehicles as well as public transport frameworks is useful. We can address complex challenges with effective results using computer vision and machine learning. The major purpose of our project is to deal with the concept of real-time-vehicle tracking is based on video stream by a surveillance camera to track vehicles. We would focus on the type of vehicle i.e., car or bike, Firstly, the software would slice the frames from the streaming video so that would be easier to detect the attributes precisely. Secondly, slice the area of the required part, for instance if we take the frame, the car in it would be cropped for focusing on the required output as correctly as possible. Lastly, the software would store the required data in the database for future usage.

Object tracking is a procedure of movement estimation in computer vision application we present a combined multiple object tracking technique for a video. A video is a frame by frame sequence of images. Optical flow is a flexible representation of visual motion that is particularly suitable for computers for analyzing digital images. In this work the Horn & Schunck method is used to find the optical flow vectors which in turn pave a way for the detection and tracking of the single moving object in a video. Kalman filter removes the noise that effects a background subtracted image and predicts the position of an object accurately. A combination of Optical flow and Kalman filter method is designed in order to attain an accurate object tracking system. The accuracy of occluded object in dynamic background is promising compared to simple background subtraction. The experiments are conducted on different videos to prove the efficiency and the results are discussed. KeywordKalman Filters (KF), Optical flow (OF), Background Subtraction, Occlusion Handling.

Improved multiple object detection and tracking using KF-OF method

Audio classification based on genre

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Improved multiple object detection and tracking using KF-OF method

Audio classification based on genre