Edge - Ama-Module e-AI Camera anama-NPU akhoyo ukuze kuqondwe izenzo ngesikhathi sangempela

In die afgelope jare het die veld van kunsmatige intelligensie (KI) merkwaardige vooruitgang getoon, en een area wat aansienlike aandag gekry het, is regte - tyd gebaarherkenning. Hierdie tegnologie het die potensiaal om verskeie nywerhede te revolusioneer, van mens - rekenaar interaksie in slim huise en motorstelsels tot toepassings in gesondheidsorg, sekuriteit en vermaak. In die hart van hierdie innovasie isedge - AI kameramodules equipped with onboard neural network processing units (NPUs). In this blog post, we will explore what these modules are, how they work, their advantages, and real - world applications.

Edge - AI kamera moduli ndi zida zazing'ono zomwe zimaphatikiza sensor ya kamera ndi mphamvu za AI. Osati makamera achikhalidwe omwe amangotenga zithunzi kapena mavidiyo ndikuzitumiza ku seva yapakati kuti ipititse patsogolo, makamera a edge - AI angathe kuchita kuyang'ana nthawi yeniyeni kwa data ya chithunzi mwachindunji pa chiyambi. Izi zikutanthauza kuti m'malo mokhala ndi chingwe cha intaneti chachikulu kuti zitumize data ku seva ya cloud yapafupi kuti ipititse patsogolo, moduli ikhoza kutenga zisankho m'njira yakomweko, kuchepetsa nthawi yochita ndi kukulitsa kuchita bwino kwa dongosolo.

An NPU, or neural network processing unit, is a specialized hardware component designed to accelerate the execution of neural network algorithms. Neural networks are the backbone of modern AI systems, especially for tasks like image recognition and gesture analysis. When integrated into an edge - AI camera module, the NPU enables the module to perform complex calculations required for real - time gesture recognition much faster than a general - purpose CPU. It is optimized for parallel processing, which is crucial for handling the large amounts of data generated by the camera sensor. For example, when a camera captures a video stream, the NPU can quickly analyze each frame to detect and classify gestures, without the need for significant external computational resources.

Die Prozess der Echtzeit-Gesten-Erkennung in Edge-AI-Kameramodulen umfasst mehrere Schritte. Zuerst erfasst die Kamera eine Reihe von Bildern oder einen Video-Stream. Die erfassten visuellen Daten werden dann vorverarbeitet, um ihre Qualität zu verbessern und sie für weitere Analysen geeignet zu machen. Dies kann Aufgaben wie Rauschreduzierung, Bildnormalisierung und Größenänderung umfassen.

Next, the pre - processed data is fed into a pre - trained neural network model. These models are typically trained on large datasets of gesture images or videos. For instance, a model may be trained on thousands of images of different hand gestures, such as a wave, a fist, or a thumbs - up. The neural network has learned to recognize patterns in these gestures during the training phase. When new data is presented to the network, it tries to match the patterns in the input data with the ones it has learned.

Dankie aan die aan boord NPU, kan die neurale netwerk die data in werklike tyd verwerk. Sodra 'n nuwe raam deur die kamera vasgevang word, begin die NPU dit analiseer. Die NPU se vermoë om parallelle berekeninge uit te voer, stel dit in staat om die invoer vinnig teen die geleerde patrone in die neurale netwerk te evalueer. As die invoer ooreenstem met 'n bekende gebaarpatroon, kan die module die ooreenstemmende gebaar etiket in 'n kwessie van millisekondes uitset. Hierdie werklike tyd verwerking is noodsaaklik vir toepassings waar onmiddellike reaksie vereis word, soos in 'n gebaar-beheerde speletjie stelsel of 'n werklike tyd teken-taal vertaal toestel.

One of the most significant advantages of using edge - AI camera modules with onboard NPUs for gesture recognition is the reduction in latency. In traditional cloud - based processing models, there is a delay between the time a gesture is made and the time the response is received. This delay is due to the time it takes to send the data from the camera to the cloud server, process it on the server, and then send the result back. With edge - AI camera modules, the processing is done locally, eliminating this round - trip delay. For example, in a virtual reality (VR) application where the user's hand gestures control the actions in the virtual environment, low latency is crucial for a seamless and immersive experience. If there is a noticeable delay between the user making a gesture and the corresponding action in the VR world, it can break the illusion and make the experience less enjoyable.

Ubumfihlo buye bube yinkinga ekhula emhlabeni wedijithali, ikakhulukazi uma kuziwa ekuqoqweni nasekucubunguleni kwedatha yomuntu siqu. Ama-module we-AI kamera e-Edge anikeza ubumfihlo obuphucukile uma kuqhathaniswa nezixazululo ezisekelwe efwini. Njengoba idatha icubungulwa endaweni kudivayisi, akudingeki ukuthumela idatha ebonakalayo ebucayi, efana nezithombe zobuso noma izandla zabantu, kwi-inthanethi. Lokhu kubaluleke kakhulu ezinhlelweni lapho ubumfihlo bubalulekile kakhulu, njengasemtholampilo lapho idatha yomgibeli kufanele ivikelwe, noma ezinhlelweni zokuphepha zezakhamuzi ezihlakaniphile lapho abanikazi bezindlu bengafuni ukuthi imisebenzi yabo yangasese ithunyelwe kumaseva angaphandle.

In vielen realen Szenarien kann die verfügbare Netzwerkbandbreite begrenzt oder unzuverlässig sein. Zum Beispiel in abgelegenen Gebieten, industriellen Umgebungen oder während Zeiten hoher Netzwerküberlastung kann eine stabile und schnelle Internetverbindung möglicherweise nicht verfügbar sein. Edge-AI-Kameramodule können in solchen Umgebungen mit niedriger Bandbreite unabhängig funktionieren. Sie sind nicht auf eine kontinuierliche und schnelle Netzwerkverbindung angewiesen, um Gestenerkennung durchzuführen. Dies macht sie in Situationen, in denen cloudbasierte Verarbeitung ineffektiv wäre, äußerst zuverlässig. In einer Fabrikumgebung, zum Beispiel, wo es Störungen mit dem drahtlosen Netzwerk geben kann, kann ein Edge-AI-Kameramodul dennoch die Gesten der Arbeiter genau erkennen, um Sicherheits- oder Betriebszwecke zu erfüllen.

Implementing a cloud - based gesture recognition system can be costly, especially when dealing with a large number of cameras or high - volume data processing. There are costs associated with data transfer, cloud storage, and the use of cloud computing resources. Edge - AI camera modules, on the other hand, can offer cost - efficiency. Once the initial investment in the hardware is made, the ongoing costs are relatively low, as there is no need to pay for continuous data transfer and cloud - based processing. This makes them an attractive option for businesses and organizations looking to implement gesture - recognition technology on a budget.

Mu makhaya akhono, ama-moduli e-camera e-edge - AI anekhono amakhono okukhombisa angaguqula indlela abantu abahlangana ngayo nezindawo zabo zokuhlala. Isibonelo, abasebenzisi bangalawula amadivayisi akhaya akhono afana nezibani, ama-thermostat, kanye nezicathulo ngezikhumbuzo ezilula zeminwe. Ukuphakamisa kwesandla kungavula izibani egumbini, noma isikhumbuzo esithile singalungisa izinga lokushisa. Lokhu kuhlinzeka ngendlela ethokozisayo futhi engenazandla yokulawula izinhlelo zokwenza ikhaya, ikakhulukazi ewusizo uma izandla zomuntu zigcwele noma uma kudingeka impendulo esheshayo.

In the automotive sector, gesture recognition can enhance the driving experience and improve safety. Edge - AI cameras installed in the car can recognize the driver's hand gestures. For instance, a simple hand gesture can be used to answer or reject a phone call, change the radio station, or adjust the volume, without the driver having to take their hands off the steering wheel. This reduces distractions and can potentially prevent accidents caused by fumbling with touchscreens or buttons while driving.

In die gesondheidsorg kan gebaarherkenningstegnologie wat deur rand-AI-kameramodules aangedryf word, help met pasiëntsorg. Byvoorbeeld, in rehabilitasiesentrums kan pasiënt se handbewegingsoefeninge in werklike tyd gemonitor word. Die kamera kan die pasiënt se gebare herken en terugvoer gee oor die akkuraatheid en vordering van hul rehabilitasieoefeninge. Dit kan gesondheidsorgverskaffers help om die pasiënt se herstel meer effektief te volg en die behandelingsplan dienooreenkomstig aan te pas.

Ibhizinisi lokuzijabulisa liphinde lamukela ubuchwepheshe bokuhlonza izenzo. Emidlalweni, abadlali bangasebenzisa izenzo zezandla zabo ukulawula abalingiswa emidlalweni, bephakamisa izinga elisha lokuxhumana. Amamojula wekhamera ye-Edge-AI avumela ukulandela izenzo zomdlali ngesikhathi sangempela, enikeza isipiliyoni sokudlala esijulile nesijabulisayo. Ngaphezu kwalokho, ezinhlelweni zokwenziwa kanye nezokwenziwa okwandisiwe, ukuhlole izenzo kuvumela abasebenzisi ukuba baxhumane nezinto ezikwi-virtual ngendlela engcono, kuthuthukisa isipiliyoni somsebenzisi jikelele.

Ngaphandle kwezinzuzo eziningi, kusekhona nezinselelo ezithile ezihambisana nezinhlelo ze-edge - AI camera modules zokuhlonza izenzo ngesikhathi sangempela. Enye yezinselelo ezinkulu ukuthuthukiswa kwemodeli ye-neural network enembile futhi eqinile. Ukuqeqesha imodeli engakwazi ukuhlole izinhlobo ezahlukene zezenzo ezimweni zokukhanya ezahlukene, ama-angles, kanye nabasebenzisi abahlukene kungaba umsebenzi onzima. Ngaphezu kwalokho, ukuqinisekisa ukuphepha kwezinsiza ze-edge - AI kubalulekile, njengoba zingase zibe sengozini yokuhlaselwa noma ukuhlaselwa okubi. Enye inselelo yizinsiza zokucubungula ezilinganiselwe ezitholakala kudivayisi ye-edge. Nakuba ama-NPU athuthukise kakhulu amandla okucubungula, kungase kube nezikhala uma kuziwa emisebenzini yokuhlonza izenzo eyinkimbinkimbi kakhulu noma idatha yevidiyo enezinga eliphezulu.

Die Zukunft von Edge - KI-Kameramodulen für die Echtzeit-Gestenkennung sieht vielversprechend aus. Da die Technologie weiterhin voranschreitet, können wir erwarten, dass leistungsstärkere und energieeffizientere NPUs entwickelt werden. Dies wird es ermöglichen, komplexere Gesten-Erkennungsalgorithmen auf Edge-Geräten auszuführen, was die Genauigkeit und Leistung der Systeme weiter verbessert. Darüber hinaus wird die Integration von Edge - KI-Kameramodulen mit anderen aufkommenden Technologien wie 5G und dem Internet der Dinge (IoT) neue Möglichkeiten für Anwendungen eröffnen. Zum Beispiel könnten in einem Szenario einer Smart City Edge - KI-Kameras mit Gesten-Erkennungsfähigkeiten verwendet werden, um den Fußgängerverkehr zu überwachen und Echtzeit-Feedback zur Verbesserung des Verkehrsflusses zu geben. Die Entwicklung benutzerfreundlicherer und anpassbarer Gesten-Erkennungssysteme wird diese Technologie auch für eine breitere Nutzer- und Branchenbasis zugänglicher machen.

Edge - AI kamera moduli anabo NPUs a'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'na'