Object Detection Techniques in Videos

doi:10.35940/ijitee.b1095.1292s19

Object Detection Techniques in Videos

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b1095.1292s19 ◽

2019 ◽

Vol 9 (2S) ◽

pp. 616-620

Keyword(s):

Anomaly Detection ◽

Object Detection ◽

Real Time ◽

Pedestrian Detection ◽

Research Attention ◽

Detection Techniques ◽

Future Work

Object detection in videos has increased its popularity because of its wider applications. It has gained more research attention now days as it is applicable in real time situations like pedestrian detection, anomaly detection, Self moving cars, sports, counting of people etc. This paper begins with the introduction of object detection and briefs the basic steps in the process. It also provides a review of various techniques and approaches used for object detection in videos. Discussion of every approach and limitations will provide several promising directions and guidelines for future work.

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Real-Time Identification of Cyber-Physical Attacks on Water Distribution Systems via Machine Learning–Based Anomaly Detection Techniques

Journal of Water Resources Planning and Management ◽

10.1061/(asce)wr.1943-5452.0001023 ◽

2019 ◽

Vol 145 (1) ◽

pp. 04018089 ◽

Cited By ~ 9

Author(s):

Ahmed A. Abokifa ◽

Kelsey Haddad ◽

Cynthia Lo ◽

Pratim Biswas

Keyword(s):

Machine Learning ◽

Anomaly Detection ◽

Real Time ◽

Distribution Systems ◽

Water Distribution ◽

Water Distribution Systems ◽

Detection Techniques ◽

Real Time Identification

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Object detection in real time based on improved single shot multi-box detector algorithm

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01826-x ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Ashwani Kumar ◽

Zuopeng Justin Zhang ◽

Hongbo Lyu

Keyword(s):

Neural Networks ◽

Object Detection ◽

Real Time ◽

Convolutional Neural Networks ◽

Single Layer ◽

Single Shot ◽

Convolutional Network ◽

Detection Techniques ◽

Computational Performance ◽

Speed Up

Abstract In today’s scenario, the fastest algorithm which uses a single layer of convolutional network to detect the objects from the image is single shot multi-box detector (SSD) algorithm. This paper studies object detection techniques to detect objects in real time on any device running the proposed model in any environment. In this paper, we have increased the classification accuracy of detecting objects by improving the SSD algorithm while keeping the speed constant. These improvements have been done in their convolutional layers, by using depth-wise separable convolution along with spatial separable convolutions generally called multilayer convolutional neural networks. The proposed method uses these multilayer convolutional neural networks to develop a system model which consists of multilayers to classify the given objects into any of the defined classes. The schemes then use multiple images and detect the objects from these images, labeling them with their respective class label. To speed up the computational performance, the proposed algorithm is applied along with the multilayer convolutional neural network which uses a larger number of default boxes and results in more accurate detection. The accuracy in detecting the objects is checked by different parameters such as loss function, frames per second (FPS), mean average precision (mAP), and aspect ratio. Experimental results confirm that our proposed improved SSD algorithm has high accuracy.

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Improved real-time data anomaly detection using context classification

Journal of Hydroinformatics ◽

10.2166/hydro.2011.042 ◽

2011 ◽

Vol 13 (3) ◽

pp. 307-323 ◽

Cited By ~ 18

Author(s):

Nemanja Branisavljević ◽

Zoran Kapelan ◽

Dušan Prodanović

Keyword(s):

Anomaly Detection ◽

Data Acquisition ◽

Real Time ◽

Water Distribution ◽

Real Life ◽

Detection Methods ◽

Time Data ◽

Detection Techniques ◽

Real Time Data ◽

Processing Techniques

The number of automated measuring and reporting systems used in water distribution and sewer systems is dramatically increasing and, as a consequence, so is the volume of data acquired. Since real-time data is likely to contain a certain amount of anomalous values and data acquisition equipment is not perfect, it is essential to equip the SCADA (Supervisory Control and Data Acquisition) system with automatic procedures that can detect the related problems and assist the user in monitoring and managing the incoming data. A number of different anomaly detection techniques and methods exist and can be used with varying success. To improve the performance, these methods must be fine tuned according to crucial aspects of the process monitored and the contexts in which the data are classified. The aim of this paper is to explore if the data context classification and pre-processing techniques can be used to improve the anomaly detection methods, especially in fully automated systems. The methodology developed is tested on sets of real-life data, using different standard and experimental anomaly detection procedures including statistical, model-based and data-mining approaches. The results obtained clearly demonstrate the effectiveness of the suggested anomaly detection methodology.

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Efficient Object Detection Model for Real-time UAV Application

Computer and Information Science ◽

10.5539/cis.v14n1p45 ◽

2021 ◽

Vol 14 (1) ◽

pp. 45

Author(s):

Subrahmanyam Vaddi ◽

Dongyoun Kim ◽

Chandan Kumar ◽

Shafqat Shad ◽

Ali Jannesari

Keyword(s):

Object Detection ◽

Real Time ◽

High Performance ◽

Class Imbalance ◽

Detection Techniques ◽

Detection Model ◽

Deep Feature ◽

Proposed Model ◽

Negative Effect ◽

Feature Pyramid

Unmanned Aerial Vehicles (UAVs) equipped with vision capabilities have become popular in recent years. Many applications have especially been employed object detection techniques extracted from the information captured by an onboard camera. However, object detection on UAVs requires high performance, which has a negative effect on the result. In this article, we propose a deep feature pyramid architecture with a modified focal loss function, which enables it to reduce the class imbalance. Moreover, the proposed method employed an end to end object detection model running on the UAV platform for real-time application. To evaluate the proposed architecture, we combined our model with Resnet and MobileNet as a backend network, and we compared it with RetinaNet and HAL-RetinaNet. Our model produced a performance of 30.6 mAP with an inference time of 14 fps. This result shows that our proposed model outperformed RetinaNet by 6.2 mAP.

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Real-time on-board pedestrian detection using generic single-stage algorithms and on-road databases

International Journal of Advanced Robotic Systems ◽

10.1177/1729881420929175 ◽

2020 ◽

Vol 17 (5) ◽

pp. 172988142092917

Author(s):

Vicent Ortiz Castelló ◽

Omar del Tejo Catalá ◽

Ismael Salvador Igual ◽

Juan-Carlos Perez-Cortes

Keyword(s):

Object Detection ◽

Real Time ◽

Pedestrian Detection ◽

Input Image ◽

Performance Comparison ◽

Detection Methods ◽

Computational Time ◽

Image Size ◽

Single Class ◽

Real Time System

Pedestrian detection is a particular case of object detection that helps to reduce accidents in advanced driver-assistance systems and autonomous vehicles. It is not an easy task because of the variability of the objects and the time constraints. A performance comparison of object detection methods, including both GPU and non-GPU implementations over a variety of on-road specific databases, is provided. Computer vision multi-class object detection can be integrated on sensor fusion modules where recall is preferred over precision. For this reason, ad hoc training with a single class for pedestrians has been performed and we achieved a significant increase in recall. Experiments have been carried out on several architectures and a special effort has been devoted to achieve a feasible computational time for a real-time system. Finally, an analysis of the input image size allows to fine-tune the model and get better results with practical costs.

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Machine Tools Anomaly Detection Through Nearly Real-Time Data Analysis

Journal of Manufacturing and Materials Processing ◽

10.3390/jmmp3040097 ◽

2019 ◽

Vol 3 (4) ◽

pp. 97 ◽

Cited By ~ 1

Author(s):

Gorka Herranz ◽

Alfonso Antolínez ◽

Javier Escartín ◽

Amaia Arregi ◽

Jon Kepa Gerrikagoitia

Keyword(s):

Anomaly Detection ◽

Data Processing ◽

Real Time ◽

Machine Tools ◽

Production Environment ◽

Time Data ◽

Detection Techniques ◽

Data Segmentation ◽

Operational Information ◽

Technical Architecture

This work presents a new methodology for machine tools anomaly detection via operational data processing. The previous methodology has been field tested on a milling-boring machine in a real production environment. This paper also describes the data acquisition process, as well as the technical architecture needed for data processing. Subsequently, a technique for operational machine data segmentation based on dynamic time warping and hierarchical clustering is introduced. The formerly mentioned data segmentation and analysis technique allows for machine tools anomaly detection thanks to comparison between near real-time machine operational information, coming from strategically positioned sensors and outcomes collected from previous production cycles. Anomaly detection techniques shown in this article could achieve significant production improvements: “zero-defect manufacturing”, boosting factory efficiency, production plans scrap minimization, improvement of product quality, and the enhancement of overall equipment productivity.

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