A Comparative Study of Object Recognition Techniques

Rani, Ritu; Kumar, Ravinder; Singh, Amit Prakash

doi:10.1109/isms.2016.43

Cited by 8 publications

(6 citation statements)

References 24 publications

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“…As the positive kinds of sensors, thanks of their inherent activeness characteristics, radar and Lidar can get the relative distance and velocity directly, however, their performance would drop dramatically in the fog, haze and rain days [18]. For the passive sensors, such as camera, they can only be used in the daytime and lost their capability in the night [19]. Meanwhile, V2V sensors were also comprehensively used in this area, including DSRC (Dedicated Short Range Communications) and IEEE 802.11p protocol [20].…”

Section: Related Workmentioning

confidence: 99%

“…Where, r c is the gain coefficient of lateral direction variance.σ y is the saturation value for limiting σ Ay in a reasonable range. Formula (18) and formula (19) obviously implies that the relatively high velocity will not only increase the possibility of the collision before exceeding the overtaken vehicle but also decrease the possibility of collision after the exceeding. Similarly, the conflict potential field of overtaken vehicles can also be constructed in accordance with the method mentioned above.…”

Section: A Conflict Potential Fieldmentioning

confidence: 99%

See 1 more Smart Citation

A Design of Cooperative Overtaking Based on Complex Lane Detection and Collision Risk Estimation

et al. 2019

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Cooperative overtaking is believed to have the capability of improving road safety and traffic efficiency by means of the real-time information exchange between traffic participants, including road infrastructures, nearby vehicles and others. In this paper, we focused on the critical issues of modeling, computation, and analysis of cooperative overtaking and made it playing a key role in the road overtaking area. In detail, for the purpose of extending the awareness of the surrounding environment, the lane markings in front of ego vehicle were detected and modeled with Bezier curve using an onboard camera. While the nearby vehicle positions were obtained through the vehicle-to-vehicle communication scheme making assure of the accuracy of localization. Then, Gaussian-based conflict potential field was proposed to guarantee the overtaking safety, which can quantitatively estimate the oncoming collision danger. To support the proposed method, many experiments were conducted on the human-in-the-loop simulation platform. The results demonstrated that our proposed method achieves better performance, especially in some unpredictable nature road circumstances.

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Section: Related Workmentioning

confidence: 99%

Section: A Conflict Potential Fieldmentioning

confidence: 99%

A Design of Cooperative Overtaking Based on Complex Lane Detection and Collision Risk Estimation

et al. 2019

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“…Identifying any precise object in a video or an image is the main objective of object recognition [13]. The proposed method recognizes the objects from the scenes through Support Vector Machine.…”

Section: Object Recognitionmentioning

confidence: 99%

Recognition and Location Estimation for Multiple Indoor Static Objects

Balamurugan¹,

Arunkumar²,

Prabakaran³

2019

IJRTE

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 Abstract: Locating objects in an image is a very useful task for robotic navigation and visually impaired persons. The ultimate goal of my work is to position the recognized objects in the image. Objects are detected using Adaboost techniques and also recognized from the real-time images. Objects are detected using AdaBoost classifier. SIFT features are extracted from the objects found in the image and classified using Support Vector Machine, and the position of an objects are estimated. We proposed IOLE algorithm to estimate the location of object in an image.

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“…HOG is similar to both SIFT and GLOH, because it uses both rectangular and log-polar location grids. The main difference between HOG and SIFT is that HOG is computed on a dense grid of uniformly spaced cells, with overlapping local contrast normalization [19]. In [20], the features of the vehicle are extracted by the proposed GIST image processing algorithm and recognized by Support Vectors Machine classifier.…”

Section: Related Workmentioning

confidence: 99%

Illumination Correction in a Comparative Analysis of Feature selection for Rear-View Vehicle Detection

Baghdadi¹,

Aboutabit²

2019

IJMLC

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One of the most frequent infractions on the road is the act of a vehicle crossing to the wrong side of the road to pass another vehicle traveling in the same direction. Automatic detection of this violation can be a challenging issue. Thus, we aim to develop a computer vision system to robustly detect forbidden overtaking observed from a fixed camera. Our approach is based on two main phases: Line Detection and Vehicle Detection. In this work, we focus on the vehicle detection stage. Here, features are extracted from the image and then classified using machine learning algorithms. In a first experiment, we constructed different models using features such as HOG (Histogram of Oriented Gradient), SURF (Speeded up Robust Features), Gabor filter and LBP (Local Binary Patterns), and machine learning classifiers as SVM (Support Vector Machines), kNN (k-Nearest Neighbor) and Decision Tree. Then, we merged the best descriptors to combine the advantages of their different robustness in order to build a strong vehicle detection model. The performances of all constructed models are evaluated on the GTI database. In a second experiment, illumination normalization techniques were applied to image database for the effective models. Thus, the first experimental results reveal that the combination HOG+ LBP+ Gabor Phase performs well with SVM. The second experimental results show that the Variational Retinex algorithm-based illumination correction provides a significant improvement in the detection rate.

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A Comparative Study of Object Recognition Techniques

Cited by 8 publications

References 24 publications

A Design of Cooperative Overtaking Based on Complex Lane Detection and Collision Risk Estimation

A Design of Cooperative Overtaking Based on Complex Lane Detection and Collision Risk Estimation

Recognition and Location Estimation for Multiple Indoor Static Objects

Illumination Correction in a Comparative Analysis of Feature selection for Rear-View Vehicle Detection

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