OpenTLD，它与其他对象检测方法有何不同？
对于那些听说过 OpenTLD 的人来说，它如何交替跟踪不同的对象？它一次只能跟踪一个对象，但如果我在同一个视频源中训练了两个或多个对象，OpenTLD 如何决定跟踪什么？在所有示例视频中，用户手动限制要跟踪的对象，然后自动跟踪。
这是否仅被视为对象跟踪器？不是物体识别系统？我对此有点困惑。
对于我的应用程序，我可以一次跟踪/检测一个对象，但前提是我可以选择切换到跟踪另一个对象。
例如，在类似 Haar 的特征设置中： 1.我有一个杯子和一本书，使用几个正反面进行训练 2. 启动我的 Haar 识别软件，该软件会拾取杯子和书本，并用正确的标签突出显示它们
理想情况下，我认为/希望/希望 OpenTLD 所做的是： 1.使用编译好的exe，绑定视频中的cup，跟踪学习 2.下一本视频装订书，跟踪学习 3. 在包含书和杯子的视频源中，我告诉程序告诉我它可以在实时视频源中检测到的所有对象。 4. 程序告诉我它检测到杯子和书，并让我选择跟踪其中之一
这是否可行？
For those that have heard of OpenTLD, how does it alternate between tracking different objects? It can only track one object at a time, but if I had two or more objects trained in the same video feed, how does OpenTLD decide what to track? In all the sample videos the user manually bounded the object to be tracked, and afterwards it was automatically tracked.
Is this considered an object tracker only? And not an object recognition system? I'm slightly confused about this.
For my applications, I'm fine with tracking/detecting one object at a time, but only if I have the option of switching over to track another object.
For example, in a Haar-like feature setup:
1. I have a cup and a book trained using several positive and negatives
2. Starting up my Haar recognition software, the software picks up both the cup and the book and highlights them with the correct labels
Ideally what I think/hope/wish that OpenTLD does is:
1. Using compiled exe, bound cup in video, track and learn
2.next bound book in video, track and learn
3. In a video feed with both the book and cup, I tell the program to tell me all the objects that it can detect in the live video feed.
4. program tells me that it detects cup and book and gives me option to track one of them
Is this feasible?
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