Acceleration of planes segmentation using normals from previous frame

2017 ◽  
Author(s):  
Azizbek Abduraimov ◽  
Pavel Gritsenko ◽  
Igor Gritsenko ◽  
Askar Seidakhmet
Keyword(s):  
Previous Frame

CenterTrack3D: Improved CenterTrack more Suitable for 3D Objects

2021 ◽  
pp. 1-32
Author(s):  
Lipeng Gu ◽  
Shaoyuan Sun ◽  
Xunhua Liu ◽  
Xiang Li
Keyword(s):  
Object Tracking ◽  
Research Field ◽  
Unmanned Vehicles ◽  
Current Frame ◽  
3D Objects ◽  
Detection And Tracking ◽  
Tracking Algorithms ◽  
Occluded Objects ◽  
Previous Frame ◽  
3D Information

Abstract Compared with 2D multi-object tracking algorithms, 3D multi-object tracking algorithms have more research significance and broad application prospects in the unmanned vehicles research field. Aiming at the problem of 3D multi-object detection and tracking, in this paper, the multi-object tracker CenterTrack, which focuses on 2D multi-object tracking task while ignoring object 3D information, is improved mainly from two aspects of detection and tracking, and the improved network is called CenterTrack3D. In terms of detection, CenterTrack3D uses the idea of attention mechanism to optimize the way that the previous-frame image and the heatmap of previous-frame tracklets are added to the current-frame image as input, and second convolutional layer of the output head is replaced by dynamic convolution layer, which further improves the ability to detect occluded objects. In terms of tracking, a cascaded data association algorithm based on 3D Kalman filter is proposed to make full use of the 3D information of objects in the image and increase the robustness of the 3D multi-object tracker. The experimental results show that, compared with the original CenterTrack and the existing 3D multi-object tracking methods, CenterTrack3D achieves 88.75% MOTA for cars and 59.40% MOTA for pedestrians and is very competitive on the KITTI tracking benchmark test set.

scholarly journals A method of iterative image normalization for tasks of visual navigation of UAVs

2019 ◽  
pp. 144-152
Author(s):  
M O Elantcev ◽  
I O Arkhipov ◽  
R M Gafarov
Keyword(s):  
Satellite Image ◽  
Visual Navigation ◽  
Transformation Matrix ◽  
Current Frame ◽  
Scale Parameters ◽  
Perspective Distortion ◽  
Shift Rotation ◽  
Previous Frame ◽  
Aerial Vehicle ◽  
Camera Position

The work deals with a method of eliminating the perspective distortion of an image acquired from an unmanned aerial vehicle (UAV) camera in order to transform it to match the parameters of the satellite image. The normalization is performed in one of the two ways. The first variant consists in the calculation of an image transformation matrix based on the camera position and orientation. The second variant is based on matching the current frame with the previous one. The matching results in the shift, rotation, and scale parameters that are used to obtain an initial set of pairs of corresponding keypoints. From this set four pairs are selected to calculate the perspective transformation matrix. This matrix is in turn used to obtain a new set of pairs of corresponding keypoints. The process is repeated while the number of the pairs in the new set exceeds the number in the current one. The accumulated transformation matrix is then multiplied by the transformation matrix obtained during the normalization of the previous frame. The final part presents the results of the method that show that the proposed method can improve the accuracy of the visual navigation system at low computational costs.

A simple microcomputer-based system for real-time analysis of cell behaviour

1987 ◽  
Vol 87 (1) ◽  
pp. 171-182
Author(s):  
J.A. Dow ◽  
J.M. Lackie ◽  
K.V. Crocket
Keyword(s):  
Real Time ◽  
Search Algorithm ◽  
Cell Types ◽  
Real Time Analysis ◽  
Lighting Conditions ◽  
Previous Frame ◽  
Real Time Tracking ◽  
Cell Densities ◽  
Different Cell Types

An image analysis package based on a BBC microcomputer has been developed, which can simultaneously track many moving cells in vitro. Cells (rabbit neutrophil leucocytes, BHK C13 fibroblasts, or PC12 phaeochromocytoma cells) are viewed under phase optics with a monochrome TV camera, and the signal digitized. Successive frames are acquired by the computer as a 640 X 256 pixel array. Under controlled lighting conditions, cells can readily be isolated from the background by binary filtering. In real-time tracking, the positions of a given cell in successive frames are obtained by searching the area around the cell's centroid in the previous frame. A simple box-search algorithm is described, which proves highly successful at low cell densities. The resilience of different search algorithms to various exceptional conditions (such as collisions) is discussed. The success of this system in real-time tracking is largely dependent upon the leisurely speed of movement of cells, and on obtaining a clean, high quality optical image to analyse. The limitations of this technique for different cell types, and the possible configurations of more sophisticated hardware, are outlined. This system provides a versatile and automated solution to the problem of studying the movement of tissue cells.

scholarly journals Accurate moving object segmentation in unconstraint videos based on robust seed pixels selection

2020 ◽  
Vol 17 (4) ◽  
pp. 172988142094727
Author(s):  
Wenlong Zhang ◽  
Xiaoliang Sun ◽  
Qifeng Yu
Keyword(s):  
Optical Flow ◽  
Moving Objects ◽  
Object Segmentation ◽  
Moving Object ◽  
Data Sets ◽  
Seed Selection ◽  
Current Frame ◽  
Moving Object Segmentation ◽  
Random Walker ◽  
Previous Frame

Due to the clutter background motion, accurate moving object segmentation in unconstrained videos remains a significant open problem, especially for the slow-moving object. This article proposes an accurate moving object segmentation method based on robust seed selection. The seed pixels of the object and background are selected robustly by using the optical flow cues. Firstly, this article detects the moving object’s rough contour according to the local difference in the weighted orientation cues of the optical flow. Then, the detected rough contour is used to guide the object and the background seed pixel selection. The object seed pixels in the previous frame are propagated to the current frame according to the optical flow to improve the robustness of the seed selection. Finally, we adopt the random walker algorithm to segment the moving object accurately according to the selected seed pixels. Experiments on publicly available data sets indicate that the proposed method shows excellent performance in segmenting moving objects accurately in unconstraint videos.

scholarly journals TEXTURE CLASSIFICATION USING WEIGHTED PROBABILISTIC NEURAL NETWORKS

2012 ◽  
pp. 126-128
Author(s):  
M. KIRANMAYEE ◽  
M. SUBBARAO
Keyword(s):  
Texture Classification ◽  
Probabilistic Neural Network ◽  
Signal To Noise Ratio ◽  
Contextual Information ◽  
The Novel ◽  
Probabilistic Neural Networks ◽  
Classification Rate ◽  
Previous Frame ◽  
Feature Based ◽  
Initial Classification

Texture classification is basically the problem of classifying pixels in an image according to their textural cues. This is different from conventional image segmentation as the texture is characterized using both the gray value for a given pixel and gray-level pattern in the neighborhood surrounding the pixel. In this project, the novel temporal updating approach is developed for weighted probabilistic neural network (WPNN) classifiers that can be used to classify the textures. This is done by utilizing the temporal contextual information and adjusting the WPNN to adapt to such changes. Whenever a new set of images arrives, an initial classification is first performed using the WPNN updated to the last frame while at the same time, a prediction using PNN is also based on the classification results of previous frame. The result of both the PNN and WPNN are then compared. Compared to the PNN, WPNN includes weighting factors between pattern layers and summation layer of the PNN. Performance of this approach is compared with model based and feature based methods in terms of signal to noise ratio and classification rate.

scholarly journals La intermediación turística en España y su vinculación con el Marketing de Afiliación: una aproximación a la realidad de las Agencias de Viajes

2015 ◽  
Vol 13 (4) ◽  
pp. 805-827
Author(s):  
Clide Rodríguez Vázquez ◽  
◽  
Mª Magdalena Rodríguez Fernández ◽  
Valentín Alejandro Martínez Fernández ◽  
Oscar Juanatey Boga ◽  
...  
Keyword(s):  
Qualitative Methodology ◽  
Primary Data ◽  
Review Of The Literature ◽  
Secondary Sources ◽  
Design And Implementation ◽  
Travel Agencies ◽  
Previous Frame ◽  
The Relationship ◽  
New Strategies ◽  
State Of Art

In the area tourist intermediation in Spain, the economic, social and technological conjuncture is playing an important role in its development, which requires the implementation of new strategies of commercialization as the affiliate marketing. The aim of this paper is based on analyzing the relationship between Spanish travel agencies and the affiliate as tool online. Owing to the specific features of the study, a review of the literature showed as more appropriate the design and implementation of a qualitative methodology, focused on getting and analyzing the primary data, which could be supplemented by other secondary sources. Regarding this, in -depth and open -ended interviews to relevant experts in the field of the study were taken. The development of such methodology allows the contrast of the pertinent hypothesis that became from the previous frame of the state of art and, therefore the extraction of their conclusions

scholarly journals Extraction of Voiced Regions of Speech from Emotional Speech Signals Using Wavelet-Pitch Method

2021 ◽  
Author(s):  
Lakshmi Srinivas Dendukuri ◽  
Shaik Jakeer Hussain
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Signal To Noise Ratio ◽  
Speech Signals ◽  
Mean Square ◽  
Emotional Speech ◽  
Statistical Parameters ◽  
Zero Crossing ◽  
Previous Frame

Extraction of voiced regions of speech is one of the latest topics in speech domain for various speech applications. Emotional speech signals contain most of the information in voiced regions of speech. In this particular work, voiced regions of speech are extracted from emotional speech signals using wavelet-pitch method. Daubechies wavelet (Db4) is applied on the speech frames after downsampling the speech signals. Autocorrelation function is performed on the extracted approximation coefficients of each speech frame and corresponding pitch values are obtained. A local threshold is defined on obtained pitch values to extract voiced regions. The threshold values are different for male and female speakers, as male pitch values are low compared to the female pitch values in general. The obtained pitch values are scaled down and are compared with the thresholds to extract the voiced frames. The transition frames between the voiced and unvoiced frames are also extracted if the previous frame is voiced frame, to preserve the emotional content in extracted frames. The extracted frames are reshaped to have desired emotional speech signal. Signal to Noise Ratio (SNR), Normalized Root Mean Square Error (NRMSE) and statistical parameters are used as evaluation metrics. This particular work provides better SNR and Normalized Root Mean Square Error values compared to the zero crossing-energy and residual signal based methods in voiced region extraction. Db4 wavelet provides better results compared to Haar and Db2 wavelets in extracting voiced regions using wavelet-pitch method from emotional speech signals.

scholarly journals Improving Performance During Camera Surveillance by Integration of Edge Detection in IoT System

2021 ◽  
Vol 12 (5) ◽  
pp. 84-96
Author(s):  
Sonal Beniwal ◽  
Usha Saini ◽  
Puneet Garg ◽  
Rakesh Kumar Joon
Keyword(s):  
Edge Detection ◽  
Surveillance System ◽  
Research Work ◽  
Detection Mechanism ◽  
Current Frame ◽  
Canny Edge Detection ◽  
Traditional System ◽  
Previous Frame ◽  
Canny Edge ◽  
Camera Surveillance

This paper is proposing an IoT-based camera surveillance system. The objective of research is to detect suspicious activities by camera automatically and take decision by comparing current frame to previous frame. Major motivation behind research work is to enhance the performance of IoT-based system by integration of edge detection mechanism. Research is making use of numerous cameras, canny edge detection-based compression module, picture database, picture comparator. Canny edge detection has been used to minimize size of graphical content to enhancing the performance system. Simulation of output of this work is made in MATLAB simulation tool. Moreover, MATLAB has been used to give comparative analysis among IoT-based camera surveillance system and traditional system. Such system requires less space, and it takes less time to inform regarding any suspicious activities.

Fast HEVC Inter-Prediction Algorithm Based on Matching Block Features

2018 ◽  
Vol 9 (1) ◽  
pp. 40-61 ◽  
Author(s):  
Meifeng Liu ◽  
Guoyun Zhong ◽  
Yueshun He ◽  
Kai Zhong ◽  
Hongmao Chen ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Motion Vector ◽  
Prediction Algorithm ◽  
Computation Method ◽  
Inter Prediction ◽  
Time Saving ◽  
Binary Decision ◽  
Binary Decision Tree ◽  
Previous Frame ◽  
Simulation Results

A fast inter-prediction algorithm based on matching block features is proposed in this article. The position of the matching block of the current CU in the previous frame is found by the motion vector estimated by the corresponding located CU in the previous frame. Then, a weighted motion vector computation method is presented to compute the motion vector of the matching block of the current CU according to the motions of the PUs the matching block covers. A binary decision tree is built to decide the CU depths and PU mode for the current CU. Four training features are drawn from the characteristics of the CUs and PUs the matching block covers. Simulation results show that the proposed algorithm achieves average 1.1% BD-rate saving, 14.5% coding time saving and 0.01-0.03 dB improvement in peak signal-to-noise ratio (PSNR), compared to the present fast inter-prediction algorithm in HEVC.

Detecting and Tracking Segmentation of Moving Objects Using Graph Cut Algorithm

2018 ◽  
pp. 177-192
Author(s):  
Raviraj Pandian ◽  
Ramya A.
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Graph Cut ◽  
Background Model ◽  
Camera Motion ◽  
Current Frame ◽  
Outdoor Environments ◽  
Previous Frame ◽  
Indoor And Outdoor Environments ◽  
Graph Cut Segmentation

Real-time moving object detection, classification, and tracking capabilities are presented with system operates on both color and gray-scale video imagery from a stationary camera. It can handle object detection in indoor and outdoor environments and under changing illumination conditions. Object detection in a video is usually performed by object detectors or background subtraction techniques. The proposed method determines the threshold automatically and dynamically depending on the intensities of the pixels in the current frame. In this method, it updates the background model with learning rate depending on the differences of the pixels in the background model of the previous frame. The graph cut segmentation-based region merging algorithm approaches achieve both segmentation and optical flow computation accurately and they can work in the presence of large camera motion. The algorithm makes use of the shape of the detected objects and temporal tracking results to successfully categorize objects into pre-defined classes like human, human group, and vehicle.

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