Surveillance cameras的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列包括價格和評價等資訊懶人包

Surveillance Practices and Mental Health: Impact of Cctv Cameras Inside Mental Health Wards

為了解決Surveillance cameras的問題，作者Desai, Suki 這樣論述：

Suki Desai has a background in social work and mental health. She has previously worked as a mental health act commissioner and regional director for the Mental Health Act Commission. More recently she has worked as a social work academic.

Surveillance cameras進入發燒排行的影片

基於語意之輪廓表示法及全連結捲積類神經網路之單晶片多車輛辨識系統

為了解決Surveillance cameras的問題，作者徐雋航 這樣論述：

鑒於現今智慧車輛發展迅速，前方車輛辨識及車距檢測為先進駕駛輔助系統 (Advanced Driver Assistance Systems, ADAS) 設計中相當重要的一環，此項技術通常藉由攝影鏡頭擷取前方影像，並透過影像辨識技術來判斷前方是否存在車輛、障礙物等等，進而控制車輛減速以保持安全距離。而這些複雜的圖形辨識技術往往需要透過高功耗之大型運算系統來實現，並且，若將傳統電腦安裝於車內常需要克服體積過大、耐震性不佳等缺點。因此，本研究專注於如何將車輛辨識及車距檢測演算法實現於單晶片，以達到高性能、低功耗，以及體積小之目的。為實現前方車輛辨識及車距檢測，本研究透過單一彩色相機模組收集前方影

像資訊，並於單一現場可程式邏輯閘陣列 (Field Programmable Gate Array, FPGA) 晶片中以最精簡之硬體電路實現白平衡 (White Balance)、影像對比度強化技術 (Image Contrast Technique)、物體邊緣檢測、利用基於模糊語意影像描述 (Semantics-based Vague Image Representation, SVIR) 改良之基於語義之輪廓表示法 (Semantic-based Contour Representation, SCR) 特徵表達物體、再透過不同的卷積核 (Convolution Kernel) 重釋SC

R特徵並交由全連接類神經網路(Fully Connected Neural Network, FCN) 進行車輛辨識。最後，以多個邊界框 (Bounding Box) 同時檢測前方多台車輛，達到單頁多目標辨識 (Single Shot MultiBox Detector，SSD) 之功能，而邊界框之座標可以透視法 (Perspective View) 計算前車相對距離。根據本研究之實驗結果，在相機以每秒90張影像攝影速度以及影像解析度在640×480像素的條件下，本研究僅須3.61us即可完成單台車輛辨識，車輛辨識率可達到94%，且車輛與非車輛至少保持38%以上之分離度，有效減少感測錯誤的情況

發生。因此，實現一真正高性能、低功耗以及體積小之前方車輛辨識晶片。

Computer Vision and Image Processing: 5th International Conference, Cvip 2020, Prayagraj, India, October 16-18, 2020, Revised Se

為了解決Surveillance cameras的問題，作者 這樣論述：

U-Net-Based Approach for Segmentation of Tables from Scanned Pages.- Air Writing: Tracking and Tracing.- Mars Surface Multi-Decadal Change Detection using ISRO’s Mars Color Camera (MCC) and Viking Orbiter Images.- Deep Over and Under Exposed Region Detection.- DeepHDR-GIF: Capturing Motion in High D

ynamic Range Scenes.- Camera Based Parking Slot Detection For Autonomous Parking.- Hard-Mining Loss based Convolutional Neural Network for Face Recognition.- Domain Adaptive Egocentric Person Re-identification.- Scene Text recognition in the wild with motion deblurring using deep networks.- Vision b

ased Autonomous Drone Navigation through enclosed spaces.- Deep Learning-based Smart Parking Management System and Business Model.- Design and Implementation of Motion Envelope for a Moving Object using Kinect for Windows.- Software Auto Trigger Recording for Super Slow Motion Videos using Statistic

al Change Detection.- Using Class Activations to Investigate Semantic Segmentation.- Few Shots Learning: Caricature to Image Recognition using Improved Relation Network.- Recognition of Adavus in Bharatanatyam Dance.- Digital Borders: Design of an Animal Intrusion Detection System based on Deep Lear

ning.- Automatic On-Road Object Detection in LiDAR-Point Cloud Data using Modified VoxelNet Architecture.- On the Performance of Convolutional Neural Networks under High and Low Frequency Information.- A Lightweight Multi-Label Image Classification Model Based on Inception Module.- Computer Vision b

ased Animal Collision Avoidance Framework for Autonomous Vehicles.- L2PF - Learning to Prune Faster.- Efficient Ensemble Sparse Convolutional Neural Networks with Dynamic Batch Size.- Inferring Semantic Object Affordances from Videos.- An Unsupervised Approach for Estimating Depth of Outdoor Scenes

from Monocular Image.- Age and Gender Prediction using Deep CNNs and Transfer Learning.- One Shot Learning Based Human Tracking in Multiple Surveillance Cameras.- Fast road sign detection and recognition using colour-based thresholding.- Dimensionality Reduction by Consolidated Sparse Representation

and Fisher Criterion with Initialization for Recognition.- Deep Learning and Density Based Clustering Methods for Road Traffic Prediction.- Deep learning based Stabbing Action Detection in ATM Kiosks for intelligent Video Surveillance Applications.- An algorithm for semantic vectorization of video

scenes.- Applications to Retrieval and Anomaly detection.- Meta-tracking and Dominant Motion Patterns at the Macroscopic Crowd Level.- Digital Video Encryption by Quasigroup on System on Chip (SoC).- Detection based Multipath Correlation Filter for Visual Object Tracking.- Graph-based depth estimati

on in a monocular image using constrained 3D wireframe models.- AE-CNN based Supervised Image Classification.- Ensemble based Graph Convolutional Network for Semi supervised learning.- Regularized Deep Convolutional Generative Adversarial Network.- A Novel Approach for Video Captioning based on Sema

ntic Cross Embedding and Skip-Connection.- Dual Segmentation Technique for Road Extraction on Unstructured Roads for Autonomous Mobile Robots.- Edge based Robust and Secure Perceptual Hashing Framework.- Real-Time Driver Drowsiness Detection Using GRU with CNN Features.- Detection of Concave Points

in Closed Object Boundaries Aiming at Separation of Overlapped Objects.- High Performance Ensembled Convolutional Neural Network for Plant Species Recognition.

融合WiFi訊號強度與人體姿態估計進行兩階段定位系統

為了解決Surveillance cameras的問題，作者鄭和軒 這樣論述：

由於近年來人們對於定位的重視，全球定位系統（Global Positioning System, GPS）已被廣泛使用於我們生活中的應用，卻礙於建築物的干擾訊號傳播導致GPS在室內定位並不準確，因此如何在室內達到高精度定位成為人們重視的研究議題，傳統的方法是以訊號強度為基礎如：藍牙、Wi-Fi、ZigBee，通過三邊測量估算裝置位置，然而，基於訊號的定位方法容易因為室內環境的多路徑干擾，導致環境中的訊號分佈變動性大，產生高定位誤差，而近年來深度學習的蓬勃發展使研究人員藉由成熟的影像辨識技術對行人進行位置估計與室內定位，卻無法得到設備資訊以識別人員身份，為此我們提出了一種基於Wi-Fi與影像的

高精度人員室內定位方法。室內定位系統分為兩階段定位，第一階段通過使用智慧型手機收集三台Wi-Fi基地台兩個頻段2.4GHz及5GHz的訊號接受強度，並以機器學習方法進行粗精度定位預測，接著在第二階段分析監視攝影機捕捉的人員畫面，並以姿態估計模型提取影像中行人們的腳點座標，再藉由直接線性轉換與線性回歸模型得到影像人員的位置，最後與第一階段的Wi-Fi定位位置進行匹配，完成可識別人員的室內定位系統。本研究採用的實驗場域具備多遮蔽物及訊號干擾，因此我們收集2.4GHz及5GHz兩個頻段的訊號接受強度，減少2.4GHz的訊號干擾以實現更高的Wi-Fi定位精度，Wi-Fi的平均定位誤差達1.4公尺，並分

析兩個頻段的定位表現。在影像定位方面我們則提出兩種用於影像中的行人腳點提取方法，並以機器學習模型減少因為鏡頭扭曲與直接線性轉換造成的誤差，結果表明我們改善後的腳點提取方法能夠降低50%的定位誤差，也指出通過機器學習模型預測的定位結果比僅以2D線性變換的誤差減少約0.4公尺，達到誤差0.4公尺的高精度室內定位。