A home surveillance system utilizing computer vision algorithms would need to implement object detection, image classification, and activity recognition capabilities. Object detection aims to identify and localize objects of a certain class (such as person, vehicle, animal) within an image or video frame. This enables the system to determine if an object of interest, like a person, is present or not.

One of the most commonly used and accurate algorithms for object detection is the Single Shot Detector (SSD). SSD uses a single deep convolutional neural network that takes an image as input and outputs bounding boxes and class probabilities for the objects it detects. It works by sliding a fixed-sized window over the image at different scales and aspect ratios, extracting features at each location using a base network like ResNet. These features are then fed into additional convolutional layers to predict bounding boxes and class scores. Some advantages of SSD over other algorithms are that it is faster, achieves higher accuracy than slower algorithms like R-CNNs, and handles objects of varying sizes well through its multi-scale approach.

For image classification within detected objects, a convolutional neural network like ResNet could be used. ResNet is very accurate for tasks like classifying a detected person as an adult male or female child. It uses residual learning blocks where identity mappings are skipped over to avoid gradients vanishing in deep networks. This allows ResNet networks to go over 100 layers deep while maintaining or improving upon the accuracy of shallower networks. Fine-tuning a pretrained ResNet model on a home surveillance specific dataset would enable the system to learn human and object classifiers tailored to the application.

Activity recognition from video data is a more complex task that requires modeling spatial and temporal relationships. Recurrent neural networks like LSTMs are well-suited for this since they can learn long-term dependencies in sequence data like videos. A convolutional 3D approach could extract spatiotemporal features from snippets of video using 3D convolutions. These features are then fed into an RNN that classifies the activity segment. I3D is a popular pre-trained 3D CNN that inflates 2D convolutional kernels into 3D to enable it to learn from video frame sequences. Fine-tuning I3D on a home surveillance activities dataset along with an LSTM could enable the system to perform tasks like detecting if a person is walking, running, sitting, entering/exiting etc from videos.

Multi-task learning approaches that jointly optimize related tasks like object detection, classification and activity recognition could improve overall accuracy since the tasks provide complementary information to each other. For example, object detections help recognize activities, while activity context provides cues to refine object classifiers. Training these computer vision models requires large annotated home surveillance datasets covering common objects, people, and activities. Data augmentation techniques like flipping, cropping, adding random noise etc. can expand limited datasets.

Privacy is another important consideration. Detection and blurring of faces, license plates etc. would be necessary before sharing footage externally to comply with regulations. Local on-device processing and intelligent alerts without storing raw footage can help address privacy concerns while leveraging computer vision. Model sizes also need to be small enough for real-time on-device deployment. Techniques like model compression, quantization and knowledge distillation help reduce sizes without large accuracy drops.

A home surveillance system utilizing computer vision would employ cutting-edge algorithms like SSD, ResNet, I3D and LSTMs to achieve critical capabilities such as person detection, identification, activity classification and more from camera views. With proper training on home surveillance data and tuning for privacy, deployment and size constraints, it has the potential to intelligently monitor homes and alert users of relevant events while respecting privacy. continued advances in models, data and hardware will further improve what computer vision enabled apps can achieve for safer, smarter homes in the future.