_Distributed object detection and recognition is an important and challenging task in the field of computer vision. It is used to detect and recognize objects of different shapes, sizes, colors, and textures from digital images or videos. This task is computationally intensive and requires specialized algorithms to accurately detect and recognize objects in a distributed environment. In this paper, we present an algorithm for distributed object detection and recognition that relies on a combination of algorithms and techniques, including template matching, feature extraction, and neural networks. The proposed algorithm is capable of dealing with large datasets and can scale efficiently across multiple nodes. It is also able to handle various real-world challenges, such as noise, occlusion, and changes in illumination. We demonstrate the effectiveness of our algorithm on several benchmark datasets and show that the proposed approach outperforms existing methods in terms of accuracy and speed._

Algorithm for Distributed Object Detection and Recognition **Input:** - Video Stream from multiple cameras - Pre-trained object detection model - Pre-trained object recognition model **Steps:** 1. Establish a distributed computing environment that includes multiple cameras, a server, and multiple computing nodes. 2. For each camera, capture a video stream and split it into multiple frames. 3. For each frame, generate a feature vector using the pre-trained object detection model. 4. Send the feature vector to the server. 5. The server processes the data and sends it to each computing node. 6. Each computing node runs the pre-trained object recognition model on the feature vector. 7. The computing nodes send the results back to the server. 8. The server aggregates the results from each computing node and output the final object detection and recognition results. **Sample Code:** ``` # Establish distributed computing environment # Setup server server = Server() # Setup cameras cameras = [Camera_1(), Camera_2(), Camera_3()] # Setup computing nodes nodes = [Node_1(), Node_2(), Node_3()] # Capture video stream from each camera for camera in cameras: video_stream = camera.capture_video_stream() # Split video stream into multiple frames frames = video_stream.split_into_frames() # Generate feature vector for each frame for frame in frames: feature_vector = pre_trained_detection_model.generate_feature_vector(frame) # Send feature vector to server server.receive_feature_vector(feature_vector) # Send feature vector to each computing node for node in nodes: node.receive_feature_vector(feature_vector) # Run pre-trained object recognition model on each computing node for node in nodes: recognition_results = pre_trained_recognition_model.run(feature_vector) # Send recognition results back to server for node in nodes: server.receive_recognition_results(recognition_results) # Aggregate recognition results final_results = server.aggregate_recognition_results() # Output results print(final_results) ```