Android Real-Time Computer Vision Developer (YOLO / Mobile AI Overlay)

@quan9824

&& YOLO, OCR, OpenCV, Tensorflow, PyTorch, Keras, ML/DL model && Hi, How are you?. I have full skills and full experiences of this field. I have developed many Image Processing project and I am expert in these fields I can finish your project with high quality and on time. Please send me your message to discuss more about your project. I am waiting your reply now. Thanks.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@himanshus642

As an Android developer with a deep understanding of AI, I can assure you that I am the perfect fit for this project. Over the course of my 5+ years career, I have built numerous scalable and automation-driven applications that perfectly resonate with the requirements you've laid out. My specialization extends to areas that are sure to significantly exponentiate your project’s value such as AI automation, secure payment integrations, API-driven systems – all fortified by a solid foundation in software system security. Specifically, on this project, not only do I posses hands-on experience deploying YOLO models on Android but also considerable knowledge of Android screen capture and overlay rendering – indispensable for building the visual overlay system you seek. Even better, my talent in optimizing mobile AI inference performance dovetails perfectly with your need for smooth real-time inference with low latency. On top of all that, I understand the importance of clear communication in ensuring a project's success thus you can rest assured of being kept informed every step of the way. To top it all off, my commitment to clean architecture will give your project the stability it needs to excel and my meticulous approach will translate into code and design cleanliness intended to surpass your expectations. Let's get started on giving life to your vision!

@sjohn117

Hi,\n\nI am Sean, an AI & Full-Stack Developer with over 10 years of experience specializing in mobile AI and computer vision solutions. Your project for a real-time visual analysis and object-tracking prototype using YOLO aligns perfectly with my expertise. I understand you need smooth, low-latency object detection and a robust overlay system for FPS environments, which are critical for user experience.\n\nIn a similar project, I successfully deployed a YOLO model on Android, achieving optimized performance with a clean architecture. My approach will involve leveraging TFLite for model deployment, combining it with OpenCV for efficient image processing, and ensuring seamless integration for real-time performance.\n\nI focus on writing clean code, implementing thorough testing, and maintaining detailed documentation to ensure a reliable and secure application. I can have the first working prototype ready within the first week, iterating based on your feedback. Can you share more about the specific environments you want to support with the app? \n\nThanks,\nSean

@shubhankar1967

Hi, I am an IITian with 10+ years of experience to develop similar projects ,I will build this using Kotlin with YOLOv8 or YOLOv10 converted to TensorFlow Lite for on-device inference, leveraging GPU delegation via NNAPI for low-latency processing. The overlay system will use Android's WindowManager with a transparent foreground service drawing Canvas-based bounding boxes synced to CameraX frame callbacks, with tracking handled by DeepSORT or ByteTrack algorithms. Configuration will parse JSON/YAML files for environment profiles, and I'll optimize the pipeline with frame skipping, model quantization (FP16/INT8), and a producer-consumer thread architecture to maintain 30+ FPS on Snapdragon 8-series devices. Kindly click on the chat button so I can share you my relavant projects. Lets connect

@ReplikaTech

Hi, yes we have experience in developing real-time AI and computer vision applications on Android using YOLO-based object detection, OpenCV, TensorFlow Lite, and mobile inference optimization frameworks. We can build a high-performance Android prototype with real-time object/player detection, overlay rendering, configurable environments, low-latency inference, and optimized mobile AI architecture compatible with modern Android devices. Our team also has experience with screen capture workflows, bounding box tracking systems, and performance tuning for FPS-level responsiveness. We are comfortable working with YOLO v8/v10 pipelines, NCNN/TFLite deployments, overlay services, and scalable configuration-based environments, and we can share relevant AI/computer vision project experience and implementation approach upon discussion.

@rahulsimraiya131

Hi, This project aligns closely with my experience building high-performance real-time systems where low-latency processing, optimized rendering, and stable mobile execution are critical. I understand the challenges involved in deploying YOLO-based detection pipelines on Android while maintaining smooth overlay performance and efficient resource usage. Full-Stack Professional with 10+ years of experience developing optimized mobile solutions, AI-integrated workflows, and performance-focused applications. Proven expertise in Android architecture, real-time processing, system optimization, and scalable deployment for demanding environments. My Questions: Are you planning to use live camera input only, or screen capture-based detection as well? Which inference stack do you prefer for deployment, TFLite, NCNN, or another optimized runtime? Let’s Connect !!

@ToufeeqX

“I HAVE DONE SOMETHING SIMILAR BEFORE!” I understand the need for clean, professional, and seamless real-time AI and computer vision development. Your project requires expertise in YOLO model deployment on Android, overlay UI integration, and performance optimization. You won’t find someone more aligned with what you’re looking for. While I’m new to Freelancer, my current priority is building strong reviews and long-term client relationships, so you’ll receive serious effort and high-quality work at a much lower rate. Come chat with me, worst case you get a free consultation :) Regards, Toufeeq

@Kartik13000

I can deliver a high-performance, low-latency Android visual analysis prototype using optimized YOLO models. Why I’m a Great Fit: Mobile Computer Vision Expertise: Strong experience deploying lightweight YOLO models (v8/v10) on Android using hardware-accelerated frameworks like TFLite / NCNN to ensure smooth, real-time inference. Stable Android Overlays: Deep understanding of Android system-level services, background screen capture APIs, and building lightweight, non-intrusive UI overlays that don't tank device performance. Configurable & Clean Code: I build modular architectures, making it easy to swap environment configurations via external JSON/YAML config files. Proposed Approach & Timeline (3–4 Weeks): Week 1: Model optimization (quantization for mobile) + Android screen capture pipeline setup. Week 2: Real-time inference integration and bounding-box overlay rendering. Week 3: Dynamic environment configuration handling + performance optimization (FPS/latency tuning). Week 4: Optional audio indicator framework + final testing/handover. I can comfortably execute this within your $500–$700 budget.

@swadeshinfotechn

As your project demands a skilled Android developer equipped with cutting-edge technology knowledge, Swadeshi Infotech is your ideal choice. My team and I bring an expansive experience in mobile app development with a strong focus on AI and computer vision, manifested in projects that cater to the challenges you've presented. We have deep familiarity with YOLO models, especially its application on Android (be it NCNN, TFLite, TensorRT, and more). To meet your project's desired timeline and quality benchmarks, we embrace innovative technologies while adhering to industry best practices. We are deeply committed to providing top-notch solutions within your budgetary parameters without compromising on creativity or performance. Our past accomplishments demonstrate this perfectly as we have strengthened startups, small businesses and enterprises through our reliable digital solutions. So let's shake hands and embark on a journey towards creating a robust FPS-style mobile visual analysis turbine together because at Swadeshi Infotech, we believe in results rather than mere promises!

@karthikresonite

Hello, Resonite Technologies has strong experience in Android AI and computer vision development, including YOLO-based real-time detection systems and mobile overlay applications. We can build your FPS-style object detection and tracking prototype with: ✔ YOLO v8/v10 integration (TFLite/NCNN/OpenCV) ✔ Real-time low-latency inference ✔ Bounding box overlay and tracking UI ✔ Configurable environments via external config files ✔ Optimized Android performance for modern devices ✔ Clean and scalable architecture Our development approach includes model optimization, overlay integration, tracking stabilization, and device-level performance tuning. Optional directional audio/event visualization can also be added. Estimated delivery: 3–4 weeks with regular milestone updates and testing. Our experienced team is ready to start immediately and provide smooth communication throughout the project. Regards, Resonite Technologies

@pauld549

As an experienced game developer with a clear understanding of the intricacies of mobile AI and computer vision, I believe I would bring immense value to your project. Over the past 5 years, I've honed my craft primarily in Unity, creating and preparing 3D assets and gameplay systems. My proficiency particularly aligns with your needs for strong deployment of YOLO models on Android, screen capture overlay rendering, and with OpenCV. My skills also include performance optimization for mobile AI inference. Moreover, my work in developing gameplay systems using C#, including FPS/TPS controllers, weapons, combat, enemy AI, interactions, UI logic, camera feel, smooth movement, and animation events is entirely applicable to the essence of your project. Lastly, a unique strength I bring to the table is combining asset creation with technical art. This skill would complement the architecture aspect of your project beautifully. With an eye for detail and optimization always in mind, let me assure you my proposed development approach aligns with your project requirements while my timely timeframe will guarantee we stay on schedule. Let's work together to bring your vision to life!

@PolarTech13

I’m an experienced Android developer specializing in real-time AI inference, computer vision, and high-performance mobile systems. Your project aligns strongly with my background in deploying YOLO-based detection pipelines and building optimized Android applications for low-latency visual processing. I can develop a clean and modular FPS-style visual analysis prototype featuring: • Real-time object/player detection using YOLOv8 or YOLOv10• High-performance Android inference pipeline using NCNN or TensorFlow Lite• Stable overlay rendering system with bounding boxes, labels, and tracking visualization• Multi-object tracking with smooth frame-to-frame consistency• Configurable runtime environments through external JSON/YAML config files• Optimized screen capture and rendering pipeline for modern Android devices• Efficient threading and GPU/NPU acceleration for minimal latency and stable FPS

@CauaAlves

Hi. I build Android overlay systems with real-time processing. My current production app uses AccessibilityService overlays with WindowManager rendering at high frame rates on mid-range devices, so I understand the performance constraints of persistent Android overlays intimately. For your project I would use: Detection: YOLOv8 deployed via NCNN (best performance on ARM) or TFLite as fallback. Model quantized to INT8 for inference under 30ms per frame on modern chipsets. Screen capture: MediaProjection API for real-time frame acquisition. Frames piped directly to the inference pipeline without intermediate disk writes. Overlay: TYPE_APPLICATION_OVERLAY with hardware-accelerated Canvas drawing for bounding boxes and tracking labels. Same rendering approach I use in production today. Config system: External JSON config files for environment parameters (model paths, confidence thresholds, overlay colors, detection classes). Hot-reloadable without app restart. Architecture: Foreground service handles capture and inference on a dedicated thread pool. Overlay renders on Main thread with minimal draw calls. Clean separation between capture, inference, and rendering layers. Optional audio directional indicator: feasible using spatial audio cues mapped to bounding box screen position. Can scope separately. I can share relevant code samples and a technical walkthrough of my overlay architecture privately if you want to evaluate before committing.

@rahmac51

Hi there, I am an AI Engineer specializing in Computer Vision and optimizing YOLO models for edge devices. I have carefully read your requirements for a real-time object-tracking prototype for FPS-style environments on Android. Relevant Experience: I have developed multiple real-time CV applications, including a "Blind Assistance System" (detecting clear paths and obstacles) and a live POS Monitoring system. I understand the critical need for zero-latency inference and stable UI overlays. Proposed Development Approach: 1. Model Optimization: I will use YOLOv8n or YOLOv10, exporting it to TFLite/NCNN to leverage Android's NNAPI/GPU delegates. This guarantees the highest FPS. 2. Frame Extraction: I will implement Android's MediaProjection API (for real-time screen capture of FPS games) or CameraX. 3. UI Overlay: I will build a transparent WindowManager overlay to draw smooth bounding boxes directly over the target without interrupting touch events. I am ready to discuss the specific config files and target objects you want to track. Best regards, Rahma

@byronw7

Hello. I’m a dedicated and results-driven freelancer from South Africa with a strong focus on delivering high-quality work that helps clients achieve real results. I take the time to fully understand each project’s goals, communicate clearly throughout the process, and provide solutions that are both professional and reliable. Your project aligns perfectly with my expertise in real-time AI and computer vision. I have a proven track record of adapting YOLO models for Android, creating user-friendly overlay systems, and optimizing performance for seamless integration. While new to Freelancer, my extensive experience off-site ensures top-notch delivery. I’m confident I can help you build a polished, high-converting system that supports your business goals. I’d love the opportunity to discuss your project further and see how I can contribute. Regards, Byron Walbrugh