Surveillance camera的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列問答集和懶人包總整理

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

為了解決Surveillance camera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.

Surveillance camera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 camera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。

Smart Camera for Traffic Surveillance

為了解決Surveillance camera的問題，作者Dembrani, Mahesh,Jayaswal, Anup,Patel, Vinit 這樣論述：

智能影像運輸系統於輔助、維護與監控應用

為了解決Surveillance camera的問題，作者呂安豐 這樣論述：

人工智慧的進步以更精密的方式迅速改變了交通系統。智慧交通系統結合行人、道路及車輛相關資訊，以提供更高的安全性、效率和舒適性。智慧交通系統中的每個元素相互牽制並都應被保留及作後續的改善。電腦視覺的最新進展在許多方面豐富了機器感知的能力。因此，本研究旨在提出一種基於智能視覺的方法，以加強智慧交通系統在協助、維護和監控方面的效果。在輔助方面，本研究著重於駕駛睡意偵測系統和交通場景分割，提出基於深度學習的即時駕駛睡意偵測系統，試圖提高極端場景下的睡意檢測品質。在交通場景分割中，利用了邊緣和特徵級別過濾方式，以達到更好的切割效果。此外，本論文提出基於知識轉移的方法來生成一個強大且有效的模型。在維護方面

，本論文提出經改良的道路裂紋檢測方法，著重於裂紋細化和具自動資源映射的高效檢測器。最後，監控方面基於視訊濃縮，能夠生成短、密集且緊湊的視頻。整體而言，上述六項成果在智慧交通系統的進步方面皆具有巨大的潛力，與文獻裏現有方法相比，本論文所提出的方法在各項指標上皆有突出的表現。