Suspicious Object Detection and Robbery Event Analysis

2007 ◽  
Author(s):  
Chi-Hung Chuang ◽  
Jun-Wei Hsieh ◽  
Kao-Chin Fan
Keyword(s):  
Object Detection ◽  
Event Analysis

scholarly journals Joint Tracking and Event Analysis for Carried Object Detection

2015 ◽  
Author(s):  
Aryana Tavanai ◽  
Muralikrishna Sridhar ◽  
Eris Chinellato ◽  
Anthony G. Cohn ◽  
David C. Hogg
Keyword(s):  
Object Detection ◽  
Event Analysis ◽  
Joint Tracking

Handheld object detection and its related event analysis using ratio histogram and mixture of HMMs

2014 ◽  
Vol 25 (6) ◽  
pp. 1399-1415 ◽  
Author(s):  
Jun-Wei Hsieh ◽  
Jiun-Chen Cheng ◽  
Li-Chih Chen ◽  
Chi-Hung Chuang ◽  
Duan-Yu Chen
Keyword(s):  
Object Detection ◽  
Event Analysis ◽  
Related Event

Carried Object Detection Using Ratio Histogram and its Application to Suspicious Event Analysis

2009 ◽  
Vol 19 (6) ◽  
pp. 911-916 ◽  
Author(s):  
Chi-Hung Chuang ◽  
Jun-Wei Hsieh ◽  
Luo-Wei Tsai ◽  
Sin-Yu Chen ◽  
Kuo-Chin Fan
Keyword(s):  
Object Detection ◽  
Event Analysis

Automatic accounting of Baikal diatomic algae: approaches and prospects

2019 ◽  
pp. 295-299
Author(s):  
Кonstantin А. Elshin ◽  
Еlena I. Molchanova ◽  
Мarina V. Usoltseva ◽  
Yelena V. Likhoshway
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Classification Accuracy ◽  
Diatom Species ◽  
Bounding Box ◽  
Synedra Acus ◽  
And Training

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

Object Detection in Cluttered Range Images Using Edgel Geometry

2010 ◽  
Vol 130 (9) ◽  
pp. 1572-1580
Author(s):  
Dipankar Das ◽  
Yoshinori Kobayashi ◽  
Yoshinori Kuno
Keyword(s):  
Object Detection ◽  
Range Images

Object Detection Using an Ideal Observer Model

2020 ◽  
Vol 2020 (16) ◽  
pp. 41-1-41-7
Author(s):  
Orit Skorka ◽  
Paul J. Kane
Keyword(s):  
Object Detection ◽  
Matched Filter ◽  
Image Sensors ◽  
Ideal Observer ◽  
Noise Power ◽  
Relative Evaluation ◽  
Detection And Identification ◽  
Cross Correlation Analysis ◽  
Observer Model ◽  
The Ideal

Many of the metrics developed for informational imaging are useful in automotive imaging, since many of the tasks – for example, object detection and identification – are similar. This work discusses sensor characterization parameters for the Ideal Observer SNR model, and elaborates on the noise power spectrum. It presents cross-correlation analysis results for matched-filter detection of a tribar pattern in sets of resolution target images that were captured with three image sensors over a range of illumination levels. Lastly, the work compares the crosscorrelation data to predictions made by the Ideal Observer Model and demonstrates good agreement between the two methods on relative evaluation of detection capabilities.

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