# Key Considerations for Real-Time Object Recognition on Edge Computing Devices
## 1 Introduction  
The proliferation of Internet of Things (IoT) devices and the increasing demand for real-time applications are driving the need for efficient object recognition capabilities at the network edge [1, 2]. Traditional cloud-based approaches face challenges in meeting the latency, bandwidth, and privacy requirements of these applications. Edge computing, which brings computation and data storage closer to the data sources, offers a promising solution by enabling local processing and reducing reliance on the cloud [3]. The proximity of edge computing can support scalability and privacy-policy enforcement for the IoT, highly responsive cloud services for mobile computing, and the capacity to conceal temporary cloud outages [4]. Integrating artificial intelligence (AI), particularly deep learning techniques, with edge computing architectures is transforming object recognition capabilities across various domains [5]. This powerful combination, known as edge AI or edge intelligence, allows for real-time object detection, tracking, and classification on resource-constrained edge devices,opening up new possibilities for intelligent systems. From smart surveillance systems that can identify suspicious activities to autonomous vehicles that can navigate complex environments, edge AI is crucial in enhancing safety, efficiency, and automation [1; 6]. The emergence of edge AI is motivated by rapid improvement in edge devices and single-board computer technology. Some edge devices with high-performance hardware accelerators are on the market, such as NVIDIA Jetson, Google Coral, FPGA, mobile device, and drone platform [7]. This emergence of edge devices is also accompanied by the emergence of special deep learning frameworks for edge devices, such as TensorflowLite [8], TensorRT [9], ONNX [10], OpenCL [11], and VitisAI [12]. Despite their potential, deploying and optimizing deep neural networks for object recognition on edge devices presents unique challenges related to the edge device, object recognition, security and performance evaluation aspects. Edge devices often have limited processing power, memory, and battery life, making it essential to design and implement AI models that are lightweight, efficient, and adaptable to these constraints [13]. Techniques like model compression, network pruning, and hardware-aware neural architecture search are crucial for tailoring deep learning models to the specific requirements of edge hardware [14]. Furthermore, strategies for task offloading, model partitioning, and distributed inference are essential for optimizing resource utilization and achieving real-time performance [15]. In terms of object recognition, there are several well-known challenges. For examples, in terms of aerial object detection using drones, there are challenges related to the object detection in challenging weather conditions [16], small object detection [17], and object detection with varying orientations [18]. In terms of security, some problems have been raised about privacy policy enforcement. Edge computing offers the opportunity to preserve privacy by not uploading all sensitive data to cloud infrastructure. The federated learning approach is one enabler which encourages the training process to be performed on the edge device, with only training weight being uploaded to cloud platform to update the global model [19]. On the other hand, there are also concerns about the reliability of edge AI systems against some well-known attacks, such as adversarial attacks and overload attacks, which can reduce the accuracy and latency performance of the systems [20; 21]. In terms of evaluation performance, edge AI is different to traditional AI, which is only evaluated based on detection accuracy performance. An excellent edge AI solution needs to balance between detection accuracy, real-time performance, energy consumption, security, and reliability, depending on the application [22; 23; 24].