AI-Powered People Detection

@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

@kaustubhbawa123

Hi, I am an IIT Grad, MongoDB Certified Developer, ex-BFSI and worked at fortune 500 companies. I will make it a reality for you. As a Full Stack Developer, I will deliver a high-quality solution using modern technologies and industry best practices. Kindly click on the chat button so we can discuss and get started. Will share you my prior projects done and my resume too. I have been doing freelancing since 2019 worked at top MNCs in both USA and India. Lets connect

@Shrutibimpl

Hello, I will design and train a computer vision model using a popular machine learning framework to detect people with high accuracy. I will prepare a diverse dataset covering both indoor and outdoor scenarios to ensure reliability across varying lighting and environmental conditions. The model will be optimized for real-time performance and integrated into a clean Python-based application. This ensures the system remains adaptable and performs consistently in the environments you specified. 1) Are you targeting specific hardware for deployment, such as an edge device or a GPU server? 2) Do you have an existing dataset of images or videos for training and validation? 3) What is the minimum frames-per-second requirement for your use case? Thanks, Bharat

@devanshkhandekar

Hi ,I’m an Applied ML Engineer who has built production grade object detection pipelines (data > training > evaluation > inference) for real-world environments where lighting, camera angle, and background shift a lot. My approach for reliable people detection (indoor + outdoor): 1. Baseline model: start with a strong detector (YOLOv9 or RT-DETR / Faster R-CNN depending on latency ,accuracy) pretrained on COCO, then fine-tune specifically for person. 2. Data,labeling strategy: audit your footage for indoor/outdoor balance, day/night, backlight, occlusion, crowd density; define clear annotation rules; add hard-negative scenes (mannequins, posters). 3. Robustness via augmentation: motion blur, low-light, rain/fog, compression artifacts, perspective jitter, occlusions so the model generalizes beyond clean images. 4. Evaluation: mAP + precision/recall target confidence + false positive analysis (reflections/shadows) and miss analysis (small/occluded people). I’ll tune thresholds/NMS per deployment. 5. Deployment: export to ONNX/TensorRT (TorchScript), provide an inference script/API, batching support, & simple monitoring hooks. Relevant experience: delivered CV models for commercial clients in detection/classification workflows (quality inspection + scene understanding), focusing on precision in messy conditions, reproducible training, and fast inference. Deliverables: trained weights, reproducible training notebook, evaluation, & a clean inference pipeline you can run locally

@chiragardeshna

Hi there, I am a strong fit for this scope because I have built and deployed people-detection models that perform reliably across mixed indoor and outdoor conditions. I have hands-on experience training and tuning object detection models for human detection using real-world datasets with varied lighting, angles, and backgrounds. I work with Python, PyTorch or TensorFlow, modern detection architectures like YOLO or similar, and structured training and evaluation pipelines. I reduce risk by validating performance separately for indoor and outdoor scenarios, controlling false positives, and delivering reproducible training and inference code. I am available to start immediately and can proceed as soon as the data and accuracy targets are defined. Regards Chirag

@lalitdeveloper7

Hi I can build and train a high-accuracy computer vision model for people detection that performs reliably in both indoor and outdoor environments, using proven object-detection techniques in Python with TensorFlow or PyTorch. The solution will include well-documented training and inference code, robust evaluation, and can be adapted to varied datasets and deployment needs. Please let me know further. Thanks

@amaank66

I can help you build a high-accuracy people-detection computer vision model that performs reliably in both indoor and outdoor environments. My approach would be to leverage proven object detection architectures (YOLOv8/YOLO-NAS or Faster R-CNN depending on accuracy vs speed needs) and fine-tune them on a diverse dataset that includes lighting variation, occlusions, crowd density, and real-world indoor/outdoor scenes. Key deliverables I will provide: • Custom-trained person detection model optimized for your deployment target (edge or cloud) • Dataset strategy + augmentation pipeline to ensure strong generalization • Full training, evaluation, and accuracy reporting (mAP, precision/recall) • Export-ready formats (ONNX, TensorRT, TFLite if needed) • Inference demo script + deployment guidance Reliability focus: • Balanced indoor/outdoor validation set • Robust performance under low light, motion blur, partial visibility • Continuous improvement loop with your real footage if available I have completed similar detection projects including human presence monitoring, safety compliance detection, and real-time surveillance analytics using PyTorch and TensorFlow. Best regards, Amaan Khan L. (CUBEMOONS PVT.)

@QuickMentor

AI-Powered People Detection I'm excited after reviewing your project details! With over 5 years of hands-on experience in Web and App Development, I specialize in building high-performing, user-friendly, and fully responsive digital solutions tailored to your business needs. I hold an academic background in Computer Science and have successfully delivered numerous projects across various industries. My expertise includes: Custom Website Development (React, Angular, Laravel, PHP, WordPress, etc.) Mobile App Development (iOS, Android, Flutter, React Native) E-commerce & CMS Solutions (Shopify, WooCommerce, Magento) API Integration & Backend Development UI/UX Design & Prototyping Bug Fixing, Speed Optimization & Maintenance ✔ Clean, Scalable & Secure Code ✔ 100% Mobile & SEO-Friendly ✔ Ongoing Support & Unlimited Revisions Let’s turn your idea into a powerful digital product that exceeds expectations! Check my profile: https://www.freelancer.com/u/QuickMentor Looking forward to working with you!

@SahilRao1247

With our strong background in AI and machine learning, your search for the right freelancer ends here. Drawing on years of experience in computer vision with a specialization in object detection, we're experts at producing high-performing models - perfectly suited to your needs of AI-powered people detection. We've particularly worked with training datasets for various environments, making us well-equipped to provide reliable performance both indoors and outdoors. Our skills don't just end at proficiency with programming languages like Python or familiarity with TensorFlow and PyTorch; rather, we bring a comprehensive expertise to the table. From video editing to 3D design and animation, we cover all aspects of enhancing visuals, giving us valuable insights into how computer vision models should perform to serve the bigger picture. In conclusion, we know our way around not just machine learning algorithms but also intricacies of real-world application. Our collaborative approach will ensure that we tailor our work to perfectly align with your expectations and deliver precise results. So let's not waste any more time and give us the opportunity to bring about a new dimension in your projects!

@amadorjay

Hello, I appreciate the opportunity to bid on your project for developing a computer vision model to detect people in various environments. I understand the importance of having a highly accurate and reliable object detection system that adapts seamlessly to both indoor and outdoor settings. With extensive experience in AI and machine learning, particularly in computer vision, I have successfully developed and deployed models that excel in object detection. My proficiency in Python, along with my familiarity with libraries like TensorFlow and PyTorch, equips me to tackle the challenges presented by your project. To ensure the successful completion of your project, I propose the following approach: - Conduct a thorough analysis of the specific requirements to tailor the model to your needs. - Utilize diverse datasets for training to enhance the model's adaptability to different environments. - Implement rigorous testing to fine-tune the model for high accuracy and reliability. - Provide ongoing support and adjustments based on your feedback and performance metrics. I am eager to start this project and confident in my ability to deliver quality results within your timeline. I would love to discuss further details and explore how I can best assist you with this initiative. Looking forward to your response!

@vishala81

I’d be glad to help with this. I’ve worked on people-detection and object-detection projects where the model had to perform reliably across very different conditions, including mixed indoor and outdoor environments with changing lighting, backgrounds, and camera angles. My usual approach is to start with a proven detection architecture (such as YOLO or a Faster R-CNN–based model in PyTorch or TensorFlow) and then fine-tune it specifically for your use case. I focus heavily on dataset quality and balance—combining public datasets with your own samples if available—to make sure the model generalises well instead of overfitting to one environment. I also validate performance separately for indoor and outdoor scenarios so accuracy is measurable and not assumed. You’ll get clean, well-structured Python code, clear training and evaluation scripts, and guidance on how to retrain or adapt the model later if conditions change. I can also help optimise the model for deployment, whether that’s on a server, edge device, or embedded system. If you’d like, I can share examples of past computer-vision work and walk you through how I’d tailor the detection pipeline specifically for people detection in your environments.

@josec298

⭐⭐⭐⭐⭐ DEAR CLIENT ⭐⭐⭐⭐⭐ Hello, I have reviewed the project details. I have extensive experience in this field and can develop a high‑accuracy YOLOv8 person detection model (PyTorch) fine‑tuned on COCO + CrowdHuman datasets for robust indoor/outdoor performance, achieving 92%+ mAP in diverse lighting/occlusion scenarios. 10-Day Plan (300 Fixed): Days 1-4: Dataset prep (COCO/CrowdHuman augment indoor/outdoor), YOLOv8 training/customisation. Days 5-8: Evaluation (mAP indoor/outdoor), ONNX export for deployment. Days 9-10: Docker inference app, docs, video demo + handover.

@WiredAI

Dear Client, WiredAI can develop a high-accuracy people-detection computer vision model optimized for GPU-based systems, including NVIDIA Jetson devices. We have strong experience in object detection for indoor and outdoor environments, ensuring reliability across varying lighting and backgrounds. We’ll use PyTorch/TensorFlow with proven architectures (YOLO, SSD, or similar), train on diverse datasets, and optimize the model for real-time performance on edge or GPU hardware. We’ve delivered similar people-detection and surveillance AI solutions and can share relevant examples upon request. Best regards, WiredAI Venture

@apkblack17

Hello, my name is Aditya Prasetya and I am here to offer you an invaluable skill set and unwavering passion for the AI world. As a Fullstack Developer, I possess considerable expertise in designing and implementing ERP applications for various industries. This experience has equipped me with a deep knowledge of Python, one of the primary languages for AI development, as well as other vital tools such as TensorFlow or PyTorch. In terms of your specific needs for object detection, I have not only honed my skills in computer vision but also am well-versed in training models on diverse datasets to ensure optimal performance in different settings. I believe this aligns perfectly with your requirement for adaptable indoor and outdoor functions. Furthermore, my proficiency in languages like PHP, JavaScript, and Pascal showcases my versatile nature and adaptability to different programming environments - a crucial quality when working with Artificial Intelligence. With my dedication to efficient and scalable solutions, you can be confident that your AI-Powered People Detection project will be delivered with the utmost precision and reliability. Let's build something extraordinary together!

@mariamz24

Hi, We went through your project description and it seems like our team is a great fit for this job. We are an expert team which have many years of experience on Java, Python, Graphic Design, Machine Learning (ML), Data Science, Computer Vision, Deep Learning, Object Detection Lets connect in chat so that We discuss further. Thank You

@sanketpatil51515

Hi, I’m Sanket, an experienced AI & Computer Vision developer with hands-on expertise in people/object detection models deployed for real-world use cases. I can build a high-accuracy person detection model that works reliably in both indoor and outdoor environments using proven deep learning approaches. How I’ll approach this Use state-of-the-art object detection models (YOLO / SSD / Faster R-CNN based on accuracy vs speed needs) Train and fine-tune on diverse datasets covering lighting, angles, crowd density, and environments Ensure strong performance across indoor (low light, occlusion) and outdoor (weather, distance) scenarios Clean, well-documented Python code using PyTorch or TensorFlow Easy inference pipeline for images, video, or live camera feeds My experience Worked on people detection, face detection, and real-time CV systems Experience with dataset preparation, augmentation, and model optimization Focus on accuracy, reliability, and production readiness I can share examples and demos of similar computer vision projects I’ve completed. Let’s discuss your accuracy targets and deployment needs, and I’ll get started. Looking forward to collaborating.

@Fel85

Hi, I’m not just proposing a detection model, but a complete and usable people-detection pipeline. My approach: • Fine-tune a proven detector (YOLOv8 / Faster R-CNN) specifically for people • Validate performance on mixed indoor + outdoor datasets • Deliver a clean Python inference pipeline (image / video / webcam) • Provide clear documentation so the model can be reused or extended Focus: • High precision under different lighting conditions • Robust detection with partial occlusion • Practical deployment, not just training accuracy Deliverables: • Trained model (PyTorch) • Inference scripts • Example demo (video or webcam) • README with setup & usage If needed, this can later be extended to real-time systems, CCTV, or edge devices. Happy to discuss your exact environment and constraints.

@kartikhalkude

worked on face recognition system using computer vision and here model to identify and detect people can be built easily

@Cassiusred

I can build this people detection model for you. I have hands-on experience with YOLO, Faster R-CNN, and custom object detection pipelines using PyTorch and TensorFlow. For your use case, I would recommend starting with YOLOv8 fine-tuned on a combined indoor/outdoor person detection dataset. This gives you the best balance of speed and accuracy, and handles varied lighting, occlusion, and crowd density well. My approach: 1. Start with a pre-trained YOLOv8 model on COCO (which already has strong person detection) 2. Fine-tune on domain-specific data matching your indoor/outdoor environments 3. Add post-processing for confidence calibration and NMS tuning 4. Package as a clean Python API with batch and real-time inference modes 5. Include evaluation metrics (mAP, precision, recall) on your test scenarios I have built similar detection systems for satellite imagery analysis and surveillance use cases. Happy to share examples. What specific environments and camera setups will this run in?