Pedestrian detection in low resolution night vision images

Pawlowski, Pawel; Piniarski, Karol; Dąbrowski, Adam

doi:10.1109/spa.2015.7365157

Cited by 17 publications

(18 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The figure shows the average of the four curves obtained through four-fold cross validation. As shown in Figure 9, the accuracies with the proposed method are higher than those using previous methods [16,42]. Next, the PPV, TPR, ACC, and F score were estimated as listed in Table 9.…”

Section: Resultsmentioning

confidence: 97%

Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors

Kim

Hong

Park

2017

Sensors

View full text Add to dashboard Cite

Because intelligent surveillance systems have recently undergone rapid growth, research on accurately detecting humans in videos captured at a long distance is growing in importance. The existing research using visible light cameras has mainly focused on methods of human detection for daytime hours when there is outside light, but human detection during nighttime hours when there is no outside light is difficult. Thus, methods that employ additional near-infrared (NIR) illuminators and NIR cameras or thermal cameras have been used. However, in the case of NIR illuminators, there are limitations in terms of the illumination angle and distance. There are also difficulties because the illuminator power must be adaptively adjusted depending on whether the object is close or far away. In the case of thermal cameras, their cost is still high, which makes it difficult to install and use them in a variety of places. Because of this, research has been conducted on nighttime human detection using visible light cameras, but this has focused on objects at a short distance in an indoor environment or the use of video-based methods to capture multiple images and process them, which causes problems related to the increase in the processing time. To resolve these problems, this paper presents a method that uses a single image captured at night on a visible light camera to detect humans in a variety of environments based on a convolutional neural network. Experimental results using a self-constructed Dongguk night-time human detection database (DNHD-DB1) and two open databases (Korea advanced institute of science and technology (KAIST) and computer vision center (CVC) databases), as well as high-accuracy human detection in a variety of environments, show that the method has excellent performance compared to existing methods.

show abstract

Section: Resultsmentioning

confidence: 97%

Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors

Kim

Hong

Park

2017

Sensors

View full text Add to dashboard Cite

show abstract

“…Correctly segmented ROIs contain all objects to be detected (pedestrians), but together have as few other objects (none pedestrians) as possible. By such means, the amount of data that is transferred to the next stages is reduced [10,29]. There are plenty of solutions for proposing the pedestrian candidates, starting from the sliding window approach in a multi-scale manner [9,10] up to more faster and intelligent solutions [8,12,30], e.g., the specialized region proposal networks [31,32].…”

Section: Ir Systems For Detection Of Pedestriansmentioning

confidence: 99%

Tuning of Classifiers to Speed-Up Detection of Pedestrians in Infrared Images

Piniarski

Pawlowski

Dąbrowski

2020

Sensors

Self Cite

View full text Add to dashboard Cite

This paper presents an experimental evaluation of real-time pedestrian detection algorithms and their tuning using the proposed universal performance index. With this index, the precise choice of various parameters is possible. Moreover, we determined the best resolution of the analysis window, which is much lower than the initial window. By such means, we can speed-up the processing (i.e., reduce the classification time by 74%). There are cases in which we increased both the processing speed and the classification accuracy. We made experiments with various baseline detectors and datasets in order to confirm versatility of the proposed ideas. The analyzed classifiers are those typically applied to detection of pedestrians, namely: aggregated channel feature (ACF), deep convolutional neural network (CNN), and support vector machine (SVM). We used a suite of five precisely chosen night (and day) IR vision datasets.

show abstract

“…Single IR camera-based detection mechanisms have been presented in [14]- [17]. Most of these approaches use HOG-based feature extraction and a classifier using SVM or other ML-based techniques [16], [18]. Paper [14] presents the use of a GMM system in human detection.…”

Section: Introductionmentioning

confidence: 99%

“…Paper [14] presents the use of a GMM system in human detection. Template matching techniques and thresholding techniques have also been reported in [16], [17].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting

Bhattarai

Martínez‐Ramón

2020

IEEE Access

View full text Add to dashboard Cite

Intelligent detection and processing capabilities can be instrumental in improving the safety, efficiency, and successful completion of rescue missions conducted by firefighters in emergency first response settings. The objective of this research is to create an automated system that is capable of real-time, intelligent object detection and recognition and facilitates the improved situational awareness of firefighters during an emergency response. We have explored state-of-the-art machine/deep learning techniques to achieve this objective. The goal of this work is to enhance the situational awareness of firefighters by effectively exploiting the infrared video that is actively recorded by firefighters on the scene. To accomplish this, we use a trained deep Convolutional Neural Network (CNN) system to classify and identify objects of interest from thermal imagery in real-time. In the midst of those critical circumstances created by a structure fire, this system is able to accurately inform the decision-making process of firefighters with upto-date scene information by extracting, processing, and analyzing crucial information. Utilizing the new information produced by the framework, firefighters are able to make more informed inferences about the circumstances for their safe navigation through such hazardous and potentially catastrophic environments. INDEX TERMS Deep convolutional neural networks, infrared images, firefighting environment, firefighters, situational awareness.

show abstract

Pedestrian detection in low resolution night vision images

Cited by 17 publications

References 11 publications

Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors

Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors

Tuning of Classifiers to Speed-Up Detection of Pedestrians in Infrared Images

A Deep Learning Framework for Detection of Targets in Thermal Images to Improve Firefighting

Contact Info

Product

Resources

About