Multimedia Concepts on Object Detection and Recognition with F1 Car Simulation Using Convolutional Layers

This paper presents a framework for detecting objects in images based on global features and contours. The first step is a shape matching algorithm that uses the background subtraction process. Object detection is accomplished by an examination of the oversegmentation of the image, where the space of the potential boundary of the object is examined to identify boundaries that have a direct resemblance to the prototype of the object type to be detected. Our analysis method removes edges using bilinear interpolation and reestablishes color sensors as lines and retracts background lines from the previous frame. Object contours are generated with clustered lines. The objects detected will then be recognized using the extraction technique. Here, we analyze the color and shape characteristics with which each object is capable of managing occlusion and interference. As an extension of object detection and recognition, F1 car simulation is experimented with simulation using various layers, such as layer drops, convolutionary layers, and boundary elimination, avoiding obstacles in different pathways.

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

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.

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

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.

A Kinematic Study of Individual Rotating Detonation Engine Waves Using K-means Algorithm

The current research is concerned with studying the instantaneous properties of the detonation waves in a RDRE by tracking each individual wave and recording its position, velocity, and peak intensity as it travels around the annulus. This information is retrieved by a non-intrusive method consisting of using a data mining technique, the k-means algorithm, to distinguish each detonation from each other in a particular frame. An algorithm was then developed to match the detonations of a current frame to the ones of a previous frame. The code was validated against results found from the back-end imaging method developed by the Air Force Research Laboratory with excellent agreement. Results for two and three-wave mode cases show that the instantaneous detonation wave speeds oscillate around the mode locked average wave speed computed from a detonation surface. Moreover, the investigation of the relationship of the detonation’s peak light intensity with the azimuthal position revealed to also be oscillatory but more distinct.

CenterTrack3D: Improved CenterTrack more Suitable for 3D Objects

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.

Unsupervised Monocular Depth Estimation for Colonoscope System Using Feedback Network

A colonoscopy is a medical examination used to check disease or abnormalities in the large intestine. If necessary, polyps or adenomas would be removed through the scope during a colonoscopy. Colorectal cancer can be prevented through this. However, the polyp detection rate differs depending on the condition and skill level of the endoscopist. Even some endoscopists have a 90% chance of missing an adenoma. Artificial intelligence and robot technologies for colonoscopy are being studied to compensate for these problems. In this study, we propose a self-supervised monocular depth estimation using spatiotemporal consistency in the colon environment. It is our contribution to propose a loss function for reconstruction errors between adjacent predicted depths and a depth feedback network that uses predicted depth information of the previous frame to predict the depth of the next frame. We performed quantitative and qualitative evaluation of our approach, and the proposed FBNet (depth FeedBack Network) outperformed state-of-the-art results for unsupervised depth estimation on the UCL datasets.

Fast HEVC Inter-Prediction Algorithm Based on Matching Block Features

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.

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

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.

Research on Lane Detection Based on Global Search of Dynamic Region of Interest (DROI)

A novel lane detection approach, based on the dynamic region of interest (DROI) selection in the horizontal and vertical safety vision, is proposed to improve the accuracy of lane detection in this paper. The curvature of each point on the edge of the road and the maximum safe distance, which are solved by the lane line equation and vehicle speed data of the previous frame, are used to accurately select the DROI at the current moment. Next, the global search of DROI is applied to identify the lane line feature points. Subsequently, the discontinuous points are processed by interpolation. To fulfill fast and accurate matching of lane feature points and mathematical equations, the lane line is fitted in the polar coordinate equation. The proposed approach was verified by the Caltech database, under the premise of ensuring real-time performance. The accuracy rate was 99.21% which is superior to other mainstream methods described in the literature. Furthermore, to test the robustness of the proposed method, it was tested in 5683 frames of complicated real road pictures, and the positive detection rate was 99.07%.

Spatial-Temporal Gaussian Scale Mixture Modeling for Foreground Estimation

Subtracting the backgrounds from the video frames is an important step for many video analysis applications. Assuming that the backgrounds are low-rank and the foregrounds are sparse, the robust principle component analysis (RPCA)-based methods have shown promising results. However, the RPCA-based methods suffered from the scale issue, i.e., the ℓ1-sparsity regularizer fails to model the varying sparsity of the moving objects. While several efforts have been made to address this issue with advanced sparse models, previous methods cannot fully exploit the spatial-temporal correlations among the foregrounds. In this paper, we proposed a novel spatial-temporal Gaussian scale mixture (STGSM) model for foreground estimation. In the proposed STGSM model, a temporal consistent constraint is imposed over the estimated foregrounds through nonzero-means Gaussian models. Specifically, the estimates of the foregrounds obtained in the previous frame are used as the prior for these of the current frame, and nonzero means Gaussian scale mixture models (GSM) are developed. To better characterize the temporal correlations, the optical flow has been used to model the correspondences between foreground pixels in adjacent frames. The spatial correlations have also been exploited by considering that local correlated pixels should be characterized by the same STGSM model, leading to further performance improvements. Experimental results on real video datasets show that the proposed method performs comparably or even better than current state-of-the-art background subtraction methods.