Object detection in videos

1. A computer-implemented method for selectively applying multiple object detection algorithms, the method being executed by one or more processors and comprising:

receiving, by the one or more processors, a video feed of a scene;

obtaining, by the one or more processors, a probability model of the scene in the video feed, wherein the probability model is based on historical detections of an object within the scene;

determining, by the one or more processors, a probability of the object being present in a first portion of the video feed and a probability of the object being present in a second portion of the video feed based on the probability model of the scene in the video feed;

selecting, by the one or more processors, the first portion of the video feed and the second portion of the video feed based on the probability of the object being present in the first portion of the video feed compared to the probability of the object being present in the second portion of the video feed;

wherein the probability of the object being present in the second portion of the video feed is greater than the probability of the object being present in the first portion of the video feed;

processing, by the one or more processors, a first portion of the video feed using a first detection algorithm to detect the object in the first portion of the video feed; and

processing, by the one or more processors, a second portion of the video feed using a second detection algorithm to detect the object in the second portion of the video feed,

wherein the first detection algorithm is different from the second detection algorithm, and

wherein the probability model includes a spatial probability model, that maps probabilities of detecting the object to regions within the scene, and a temporal probability model that maps probabilities of detecting the object to time periods.

2. The method of claim 1 , wherein the first portion of the video feed is a first set of frames and the second portion of the video feed is a second set of frames.

3. The method of claim 1 , wherein the first portion of the video feed is a first region of the scene and the second portion of the video feed is a second region of the scene.

4. The method of claim 1 , wherein the first portion of the video feed is a first region of the scene in a series of frames and the second portion of the video feed is a second region of the scene in the series of frames.

5. The method of claim 1 , wherein the first portion of the video feed is a first region of the scene in a series of frames and the second portion of the video feed is a second region of the scene in the series of frames, and

wherein selecting the first portion of the video feed and the second portion of the video feed comprises:

determining a budget of available computing resources; and

selecting a size of the first region and a size of the second region based on the budget of available computing resources.

6. The method of claim 1 , wherein the probability model includes probabilities of detecting the object mapped to regions within the scene.

7. The method of claim 1 , further comprising:

detecting the object in the video feed; and

in response to detecting the object, modifying the probability model based on a region within the scene in which object was detected.

8. The method of claim 1 , further comprising:

detecting motion in a first frame of the video feed; and

determining, based on the detected motion in the first frame, the probability of the object being present in a second frame of the video, and

wherein the second frame is selected as the second portion of the video feed.

9. The method of claim 1 , further comprising detecting the object in a region of a first frame of the video feed, and

wherein selecting the first portion of the video feed and the second portion of the video feed comprises selecting the second portion of the video feed to include a region of a second frame that is proximate to the region of the first frame, wherein the second frame is subsequent in time to the first frame.

10. The method of claim 1 , wherein the first portion includes the second portion.

11. The method of claim 1 , wherein the object is a face, wherein the first detection algorithm is a first facial detection algorithm, and wherein the second detection algorithm is a second facial detection algorithm.

12. The method of claim 1 , wherein the video feed is a live video feed.

13. The method of claim 1 , wherein the video feed is a recorded video feed.

14. A system for selectively applying multiple object detection algorithms, the system comprising:

one or more computers; and

a computer-readable medium coupled to the one or more computers having instructions stored thereon which, when executed by the one or more computers, cause the one or more computers to perform operations comprising:

receiving a video feed of a scene;

obtaining a probability model of the scene in the video feed, wherein the probability model is based on historical detections of an object within the scene;

determining a probability of the object being present in a first portion of the video feed and a probability of the object being present in a second portion of the video feed based on the probability model of the scene in the video feed;

selecting the first portion of the video feed and the second portion of the video feed based on the probability of the object being present in the first portion of the video feed compared to the probability of the object being present in the second portion of the video feed;

wherein the probability of the object being present in the second portion of the video feed is greater than the probability of the object being present in the first portion of the video feed;

processing a first portion of the video feed using a first detection algorithm to detect the object in the first portion of the video feed; and

processing a second portion of the video feed using a second detection algorithm to detect the object in the second portion of the video feed,

wherein the first detection algorithm is different from the second detection algorithm, and

wherein the probability model includes a spatial probability model, that maps probabilities of detecting the object to regions within the scene, and a temporal probability model that maps probabilities of detecting the object to time periods.

15. A non-transient computer readable storage device storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

receiving a video feed of a scene;

obtaining a probability model of the scene in the video feed, wherein the probability model is based on historical detections of an object within the scene;

determining a probability of the object being present in a first portion of the video feed and a probability of the object being present in a second portion of the video feed based on the probability model of the scene in the video feed;

selecting the first portion of the video feed and the second portion of the video feed based on the probability of the object being present in the first portion of the video feed compared to the probability of the object being present in the second portion of the video feed;

wherein the probability of the object being present in the second portion of the video feed is greater than the probability of the object being present in the first portion of the video feed;

processing a first portion of the video feed using a first detection algorithm to detect the object in the first portion of the video feed; and

processing a second portion of the video feed using a second detection algorithm to detect the object in the second portion of the video feed,

wherein the first detection algorithm is different from the second detection algorithm, and

wherein the probability model includes a spatial probability model, that maps probabilities of detecting the object to regions within the scene, and a temporal probability model that maps probabilities of detecting the object to time periods.